Abstract: Responsible Generative AI and Local LLMs Learning Generative AI During Live Coding This video series covers mastering the theory of Generative AI and using local LLMs like Mistral, llamafile, Candle, and Lorax. Lessons covered include: Profit Sharing Concepts Tragedy of the Commons Game Theory and Generative AI Perfect Competition Negative Externalities Fine-tuning Mistral with Ludwig Getting Started with llamafile Hello World in Candle Exploring Rust and Candle Transcribing Audio with Whisper Rust for LLMs Learning Objectives Learn about responsible and ethical uses of generative AI Getting hands-on with local LLMs like Mistral, llamafile, Candle, and Lorax Learning to build solutions by coding along during tutorials Learning the key syntax and features of the Rust Language Additional Popular Resources Assimilate OpenAI 52 Weeks of AWS-The Complete Series Microsoft Azure Fundamentals (AZ-900) Certification Rust Bootcamp Python Bootcamp Google Professional Machine Learning Engineer Course 2023 (Rough Draft) Rust Data Engineering Building with the GitHub EcoSystem: Copilot, CodeSpaces, and GitHub Actions Microsoft Azure Fundamentals (AZ-900) Certification Google Professional Cloud Architect Certification Course 2023 (Rough Draft) AWS Solutions Architect Professional (SAP-C02) 2023.

Abstract: MySQL for Data Engineering Learning Linux Hacker Style Coding Description: Extract MySQL data through queries and bash pipelines Dump query results to CSV files. Build web services to serve data with Python standard library Analyze and process extracts with Linux utilities Chain MySQL, Python, and Linux together for quick data tasks Additional Popular Resources Assimilate OpenAI 52 Weeks of AWS-The Complete Series Microsoft Azure Fundamentals (AZ-900) Certification Rust Bootcamp Python Bootcamp Google Professional Machine Learning Engineer Course 2023 (Rough Draft) Rust Data Engineering Building with the GitHub EcoSystem: Copilot, CodeSpaces, and GitHub Actions Microsoft Azure Fundamentals (AZ-900) Certification Google Professional Cloud Architect Certification Course 2023 (Rough Draft) AWS Solutions Architect Professional (SAP-C02) 2023.

Abstract: Introduction to Generative AI Get started with Generative AI and Large Language Models This introductory course is designed for beginners with no prior knowledge of generative AI. You will start by gaining a high-level understanding of what generative AI is and how it works. Through interactive lessons and hands-on examples, you will learn fundamental skills like providing effective prompts and iteratively improving the generated outputs. As the course progresses, you will dive deeper into specific major generative AI models, including their unique capabilities and limitations. Finally,, you will get practical experience using leading systems like GitHub Copilot, Qdrant, and OpenAI to generate code, and text. By the end, you will have developed core knowledge to start experimenting with generative AI in a responsible and effective way for a variety of applications. This course aims to provide a friendly introduction to prepare complete beginners for further exploration of this rapidly evolving technology. Learning objectives Learn to utilize Generative AI effectively Develop Generative AI software solutions Build solutions with Prompt Engineering to enhance Generative AI output Practice Labs Use the practice labs to test your knowledge and gain hands-on experience working with Python and Rust. These labs are based on GitHub templates that will help you to get started quickly and apply the knowledge in the course in building your own solutions. Learn Retrieval Augmented Generation: https://github.com/alfredodeza/learn-retrieval-augmented-generation Azure Chat with OpenAI: https://github.com/alfredodeza/azure-chat-demo Resources Azure LLMOps Practical MLOps book Rust Bootcamp Python Bootcamp.

Abstract: Interactive Technical Projects A weekly updated series of tutorials on interactive technical projects Learning Objectives Learn practical skills by building real-world projects with cutting-edge technologies Learning to build full stack solutions with languages like Rust, Python, LLMs and Data Science Learning the key tools and techniques used by industry experts to create robust applications Get hands-on practice applying your core technical knowledge Understand how all the pieces fit together by building projects end-to-end Troubleshoot bugs and solve complex problems that appear in production systems Expand your portfolio and r©♭sum©♭ with impressive projects Additional Popular Resources Assimilate OpenAI 52 Weeks of AWS-The Complete Series Microsoft Azure Fundamentals (AZ-900) Certification Rust Bootcamp Python Bootcamp Google Professional Machine Learning Engineer Course 2023 (Rough Draft) Rust Data Engineering Building with the GitHub EcoSystem: Copilot, CodeSpaces, and GitHub Actions Microsoft Azure Fundamentals (AZ-900) Certification Google Professional Cloud Architect Certification Course 2023 (Rough Draft) AWS Solutions Architect Professional (SAP-C02) 2023.

Abstract: GenAI and LLMs on AWS Learn to build machine learning pipelines leveraging large language models on AWS for natural language processing and generative AI. In this hands-on course, you will gain skills to: * Set up cloud-based Rust development environments * Construct serverless workflows with AWS Lambda and Step Functions * Generate text and code with models like Claude and CodeWhisperer * Orchestrate models and data workflows with Amazon Bedrock * By completing real-world coding projects, you will be prepared to operationalize LLMs for machine learning applications. Additional Popular Resources Assimilate OpenAI 52 Weeks of AWS-The Complete Series Microsoft Azure Fundamentals (AZ-900) Certification Rust Bootcamp Python Bootcamp Google Professional Machine Learning Engineer Course 2023 (Rough Draft) Rust Data Engineering Building with the GitHub EcoSystem: Copilot, CodeSpaces, and GitHub Actions Microsoft Azure Fundamentals (AZ-900) Certification Google Professional Cloud Architect Certification Course 2023 (Rough Draft) AWS Solutions Architect Professional (SAP-C02) 2023.

Abstract: Small Language Models and LLamaFile Learning About Small Language Models and Running Them Locally with LLamaFile This video series covers small language models, their advantages, and how to run them locally using the LLamaFile tool. Lessons Covered Include: 1.1 Introduction to Small Language Models 1.2 Demo: Running Phi Small Language Model with LLamaFile 2.1 Getting Started with LLamaFile 2.2 Demo: Running Lava Multi-Modal Vision Model with LLamaFile Learning Objectives Understand what small language models are and their advantages Learn how to run local language models using the LLamaFile tool See demos of running different small language models like Phi and Lava Additional Popular Resources Assimilate OpenAI 52 Weeks of AWS-The Complete Series Microsoft Azure Fundamentals (AZ-900) Certification Rust Bootcamp Python Bootcamp Google Professional Machine Learning Engineer Course 2023 (Rough Draft) Rust Data Engineering Building with the GitHub EcoSystem: Copilot, CodeSpaces, and GitHub Actions Microsoft Azure Fundamentals (AZ-900) Certification Google Professional Cloud Architect Certification Course 2023 (Rough Draft) AWS Solutions Architect Professional (SAP-C02) 2023.

Abstract: Agile with AI Enhancing Agile Methodologies with Artificial Intelligence This video course explores the integration of Agile practices with Artificial Intelligence (AI) to streamline software development processes and improve productivity. By combining the principles of Agile and the capabilities of AI, you can unlock new levels of efficiency and innovation. Lessons Covered Include: 1.0 Course Introduction 2.1 Waterfall Planning 2.2 Playground vs. Plant Analogy 3.2 Pragmatic Agile 3.15 Productivity Techniques 4.1 AIOps (Artificial Intelligence for IT Operations) 4.2 Diagram Prompt Engineering 4.3 Summarizing Text with Claude 4.4 AI Pair Programming: Paradigm Shift 4.5 AWS CodeWhisperer for Rust 4.7 Serverless Bedrock Diagram 4.8 Bedrock Knowledge Agent: Retrieval-Augmented Generation (RAG) 4.9 Building Blocks for Generative AI: WhisperCPP RAW Learning Objectives Understand the foundations of Agile methodologies and their importance in software development Learn techniques for enhancing Agile practices with AI tools and techniques Explore AI-powered productivity techniques for streamlining development workflows Discover AI-driven approaches to pair programming, code generation, and prompt engineering Gain insights into cutting-edge AI applications in software development, such as AIOps and knowledge agents Learn to leverage AI tools like AWS CodeWhisperer and Retrieval-Augmented Generation (RAG) models Additional Popular Resources Assimilate OpenAI 52 Weeks of AWS-The Complete Series Microsoft Azure Fundamentals (AZ-900) Certification Rust Bootcamp Python Bootcamp Google Professional Machine Learning Engineer Course 2023 (Rough Draft) Rust Data Engineering Building with the GitHub EcoSystem: Copilot, CodeSpaces, and GitHub Actions Microsoft Azure Fundamentals (AZ-900) Certification Google Professional Cloud Architect Certification Course 2023 (Rough Draft) AWS Solutions Architect Professional (SAP-C02) 2023.

Abstract: Linux for Beginners A hands on and practical introduction of Linux This early release (ongoing) course will give you a foundational knowledge to quickly get started with Linux. You will learn practical examples packaged in easy-to-follow videos that give you just enough knowledge to use Linux. There is a strong focus on using the terminal exclusively for this course, which is the best way to ensure a wide variety of Linux environments can be used with almost no difference in concepts. The course will not cover deep Linux internals or advanced usage. It will also not go through specialized scenarios that are meant to pass a Linux certification. Learn objectives Use a terminal to navigate the file system Understand different Linux versions and use readily available versions without installing Linux Use a shell with common features Create and edit files using a terminal text editor Manage system processes and services Install and remove packages About your instructor Alfredo Deza has over a decade of experience as a Software Engineer doing DevOps, automation, and scalable system architecture. Before getting into technology he participated in the 2004 Olympic Games and was the first-ever World Champion in High Jump representing Peru. He currently works in Developer Relations at Microsoft and is an Adjunct Professor at Duke University. This solid background in technology and teaching, including his past experience as a Linux system administrator is seen throughout this course, where you will get a first-hand experience with high-level knowledge and practical examples. Resources Pytest Master Class Practical MLOps book.

Abstract: DevOps command-line tools in Python and Rust Use both Python and Rust to automate systems engineering tasks In this introductory course, you will learn how to build powerful command-line tools using Python and Rust. You will discover how to set up a development environment, incorporate user input, expand functionality using modules and libraries, and optimize performance for common DevOps and Systems Engineering tasks. Whether you're new to programming or an experienced developer, this course will teach you the fundamentals of building command-line tools using Python and Rust. By the end of the course, you'll have the skills to automate tasks and improve the efficiency of your DevOps and Systems Engineering workflows. This course includes GitHub repositories that you can use as a reference for all the learning content: Python CLI Examples Rust CLI Examples Learn objectives Set up a development environment for building command-line tools. Build basic command-line tools. Expand the functionality of command-line tools using modules and libraries. Create a simple command-line tool using Rust and understand the basic structure of a Rust-based CLI tool. Incorporate user input, such as arguments and options. Learn how to organize code into modules and packages, and work with dependencies and libraries in both Python and Rust. About your instructor Alfredo Deza has over a decade of experience as a Software Engineer doing DevOps, automation, and scalable system architecture. Before getting into technology he participated in the 2004 Olympic Games and was the first-ever World Champion in High Jump representing Peru. He currently works in Developer Relations at Microsoft and is an Adjunct Professor at Duke University. This solid background in technology and teaching, including his past experience as a Linux system administrator is seen throughout this course, where you will get a first-hand experience with high-level knowledge and practical examples. Resources Pytest Master Class Practical MLOps book.

Abstract: Hugging Face for MLOps Learn how to leverage Hugging Face and its powerful machine learning capabilities. Build, train, and deploy your own models using the Hugging Face platform and libraries. In this course you'll get hands-on with Hugging Face and learn how to: Access and use pre-trained models from the Hub Fine-tune models with your own data Build machine learning pipelines with Hugging Face Transformers Add and version your own datasets Containerize and deploy Hugging Face models Automate workflows with GitHub Actions By the end, you'll have practical experience building, training, and deploying Hugging Face models, including production deployment to the Azure cloud. Learning objectives Find and use pre-trained models Fine-tune models for custom tasks Build ML pipelines with Hugging Face libraries Create and version datasets Containerize models for production Automate workflows for MLOps Lesson 1: Getting Started with Hugging Face Lesson Outline Overview of Hugging Face Hub Browsing models and datasets Using Hugging Face repositories Managing spaces and access Lesson 2: Applying Hugging Face Models Lesson Outline Downloading models from the Hub Using models with PyTorch/TensorFlow Leveraging tokenizers and pipelines Performing inference with Hub models Lesson 3: Working with Datasets Lesson Outline Browsing datasets on Hugging Face Uploading and managing datasets Versioning datasets with dataset cards Loading datasets in PyTorch/TensorFlow Lesson 4: Model Serving and Deployment Lesson Outline Containerizing Hugging Face models Creating inference APIs with FastAPI Deploying to cloud services like Azure Automating with GitHub Actions About your instructor Alfredo Deza has over a decade of experience as a Software Engineer doing DevOps, automation, and scalable system architecture. Before getting into technology he participated in the 2004 Olympic Games and was the first-ever World Champion in High Jump representing Peru. He currently works in Developer Relations at Microsoft and is an Adjunct Professor at Duke University. This solid background in technology and teaching, including his experience teaching and authoring content about LOps will give you everything you need to get started applying these powerful concepts. Resources MLOps with Databricks Introduction to MLflow for MLOps Hands-on Python for MLOps.

Abstract: AWS Certified Security - Specialty Exam Prep This video series provides comprehensive preparation for the AWS Certified Security - Specialty (SCS-C02) exam. Lessons Covered Include: Domain 1: Threat Detection and Incident Response 1.0 Introduction 1.1 Designing and Implementing Incident Response Plans 1.2 Detecting Security Threats and Anomalies with AWS Services 1.3 Responding to Compromised Resources and Workloads 1.4 Automating Incident Response with AWS Lambda 1.5 Conducting Root Cause Analysis with Amazon Detective 1.6 Capturing Forensics Data from Compromised Resources 1.7 Querying Logs to Validate Security Events 1.8 Preserving Forensic Artifacts with S3 Object Lock 1.9 Preparing and Recovering Services After Incidents 1.10 Incident Response Case Study 1.11 Practice Exam - Threat Detection and Response 1.12 Threat Detection and Incident Response Summary Learning Objectives Understand best practices for incident response in the cloud Explore AWS services to detect security threats Learn how to respond to compromised resources Domain 2: Security Logging and Monitoring 2.0 Introduction 2.1 Designing and Implementing Monitoring and Alerting 2.2 Troubleshooting Security Monitoring and Alerting 2.3 Designing and Implementing Logging Solutions 2.4 Troubleshooting Logging Solutions 2.5 Designing Log Analysis Solutions 2.6 Configuring AWS CloudTrail for Logging 2.7 Analyzing Logs with Amazon Athena 2.8 Monitoring Case Study 2.9 Practice Exam - Logging and Monitoring 2.10 Optimizing Log Storage Lifecycles 2.11 Third Party Log Analysis Tools 2.12 Security Logging and Monitoring Summary Learning Objectives Understand how to set up monitoring and alerts for security Learn best practices for logging and log analysis Gain skills in troubleshooting logging and monitoring Domain 3: Infrastructure Security 3.0 Introduction 3.1 Designing and Implementing Edge Service Security 3.2 Designing and Implementing Network Security 3.3 Designing and Securing Compute Workloads 3.4 Troubleshooting Network Security 3.5 Securing EC2 Instances with SSM 3.6 Scanning Images for Vulnerabilities 3.7 Infrastructure Security Case Study 3.8 Practice Exam - Infrastructure Security 3.9 Integrating AWS Firewall Manager 3.10 Applying Security Best Practices to Lambdas 3.11 Hardening AMIs with EC2 Image Builder 3.12 Infrastructure Security Summary Learning Objectives Learn how to secure AWS network infrastructure Understand options for compute and workload security Gain skills in troubleshooting infrastructure issues Domain 4: Identity and Access Management 4.0 Introduction 4.1 Designing Authentication for AWS Resources 4.2 Designing Authorization for AWS Resources 4.3 Troubleshooting with IAM Access Analyzer 4.4 Enforcing Least Privilege Access 4.5 Separating Duties with IAM Roles 4.6 Identity and Access Management Case Study 4.7 Practice Exam - Identity and Access Management 4.8 Securing Root User Credentials 4.9 IAM Policy Simulator for Troubleshooting 4.10 Enabling Federated Access with SSO 4.11 Managing Credentials and Secrets 4.12 Identity and Access Management Summary Learning Objectives Master IAM best practices for authentication and authorization Understand how to apply least privilege and separation of duties Learn to troubleshoot IAM issues Domain 5: Data Protection 5.0 Introduction 5.1 Data in Transit Confidentiality and Integrity 5.2 Data at Rest Confidentiality and Integrity 5.3 Managing Data at Rest Lifecycles 5.4 Protecting Credentials, Secrets, and Keys 5.5 Encrypting Data with AWS KMS 5.6 Data Protection Case Study 5.7 Practice Exam - Data Protection 5.8 Enforcing Data Retention with Glacier 5.9 Preventing Data Deletion with S3 Object Lock 5.10 Rotating RDS Credentials 5.11 Encrypting Data in Transit with SSL/TLS 5.12 Data Protection Summary Learning Objectives Learn techniques to protect data in transit and at rest Understand how to manage data lifecycles Gain skills in managing credentials and secrets Domain 6: Management and Security Governance 6.0 Introduction 6.1 Strategies for Central Account Deployment 6.2 Secure and Consistent Cloud Deployment 6.3 Evaluating Compliance of AWS Resources 6.4 Identifying Security Gaps through Reviews and Cost 6.5 AWS Organizations SCP Best Practices 6.6 Governance Case Study 6.7 Practice Exam - Governance 6.8 Automating Security Deployments 6.9 Tagging Resources for Governance 6.10 AWS Config for Security Assessments 6.11 AWS Well Architected Tool 6.12 Governance and Security Management Summary Learning Objectives Understand best practices for governing accounts and resources Learn how to align security with organizational requirements Gain skills in compliance, auditing, and deployment Additional Popular Resources Assimilate OpenAI 52 Weeks of AWS-The Complete Series Microsoft Azure Fundamentals (AZ-900) Certification AWS Solutions Architect Professional (SAP-C02) 2023 Assimilate Python From Zero.

Abstract: Rust Data Engineering: Course Description Are you a data engineer, software developer, or a tech enthusiast with a basic understanding of Rust, seeking to enhance your skills and dive deep into the realm of data engineering with Rust? Or are you a professional from another programming language background, aiming to explore the efficiency, safety, and concurrency features of Rust for data engineering tasks? If so, this course is designed for you. While a fundamental knowledge of Rust is expected, you should ideally be comfortable with the basics of data structures and algorithms, and have a working understanding of databases and data processing. Familiarity with SQL, the command line, and version control with git is advantageous. This four-week course focuses on leveraging Rust to create efficient, safe, and concurrent data processing systems. The journey begins with a deep dive into Rust's data structures and collections, followed by exploring Rust's safety and security features in the context of data engineering. In the subsequent week, you'll explore libraries and tools specific to data engineering like Diesel, async, Polars, and Apache Arrow, and learn to interface with data processing systems, REST, gRPC protocols, and AWS SDK for cloud-based data operations. The final week focuses on designing and implementing full-fledged data processing systems using Rust. By the end of this course, you will be well-equipped to use Rust for handling large-scale data engineering tasks, solving real-world problems with efficiency and speed. The hands-on labs and projects throughout this course will ensure you gain practical experience, putting your knowledge into action. This course is your gateway to mastering data engineering with Rust, preparing you for the next level in your data engineering journey. Learning Rust During Live Coding This video series covers live coding the Rust language iteratively, thus learning the language. Lessons Covered Include: Section 1: Rust Data Structures: Collections Lesson 1: Getting Started With The Modern Rust Development Ecosystem Meet the instructor & Course Overview: 1.0-meet-instructor-course-overview.mp4 Introduction to the AI Coding Paradigm Shift: 1.1-ai-pair-programming-paradigm-shift.mp4 Introduction to cloud-based development environments: 1.2-GitHub-Codespaces-Ecosystem-with-copilot-chat.mp4 Introduction to GitHub Copilot Ecosystem for Rust: 1.3-copilot-enabled-rust.mp4 Prompt Engineering with GCP BigQuery SQL: 1.4-big-query-prompt-engineering.mp4 Introduction to AWS CodeWhisperer for Rust: 1.5-aws-codewhisperer-for-rust.mp4 Using Google Bard to Enhance Productivity: 1.6-using-bard-to-enhance-productivity.mp4 Continuous Integration with Rust and GitHub Actions: 1.7-continuous-integration-rust-github-actions.mp4 Lesson 2: Rust Sequences and Maps Introducing Rust Sequences and Maps: 1.8-rust-sequences-maps.mp4 Print Rust data structures demo: 1.9-Print-Rust-data-structures-demo.mp4 Vector Fruit Salad demo: 1.10-Vector-Fruit-Salad-demo.mp4 VecDeque Fruit Salad demo: 1.11-VecDeque-Fruit-Salad-demo.mp4 Linkedin List Fruit Salad demo: 1.12-Linkedin-List-Fruit-Salad-demo.mp4 Fruit Salad CLI demo: 1.13-Fruit-Salad-CLI-demo.mp4 HashMap frequency counter demo: 1.14-HashMap-frequency-counter-demo.mp4 HashMap language comparison: 1.15-HashMap-language-comparison.mp4 Lesson 3: Rust Sets, Graphs and Miscellaneous Data Structures Analyzing UFC Fighter Network Using Graph Centrality in Rust: 1.16-ufc-graph-centrality.mp4 Storing Unique Fruits Using HashSet in Rust: 1.17-unique-fruits-with-hashset.mp4 Maintaining Sorted and Unique Fruits Using BTreeSet in Rust: 1.18-sorted-unique-fruits-with-btreeset.mp4 Creating a Fig Priority Fruit Salad Using Binary Heap in Rust: 1.19-fig-priority-fruit-salad-with-binary-heap.mp4 PageRank algorithm for sports data: 1.20-pagerank-sports.mp4 Showing shortest path with dijkstra: 1.21-shortest-path.mp4 Detecting Strongly Connected Components: A Deep Dive into Kosaraju's Algorithm: 1.22-strongly_connected_components_with_kosaraju.mp4 Simple Charting of Data Structures in Rust: 1.23-ascii-graphing.mp4 Section 2: Safety, Security and Concurrency with Rust Lesson 1: Rust Safety and Security Features Multi-Factor Authentication: 2.1_Multi-Factor_Authentication.mp4 Network Segmentation: 2.2_Network_Segmentation.mp4 Least Privilege Access: 2.3_Least_Privilege_Access.mp4 Encryption: 2.4_Encryption.mp4 Mutable fruit salad: 2.5-mutable-fruit-salad.mp4 Customize fruit salad with a CLI: 2.6-customize-csv-fruit-salad.mp4 Data Race example: 2.7-data-race.mp4 Lesson 2: Security Programming with Rust High Availability: 2.10-high-availability.mp4 Understanding the Homophonic Cipher: A Cryptographic Technique: 2.11-homphonic-cipher.mp4 Decoding the Secrets of the Caesar Cipher: 2.12-caesar-cipher.mp4 Building a Caesar Cipher Command Line Interface: 2.13-caesar-cipher-cli.mp4 Creating a Decoder Ring: A Practical Guide: 2.14-decoder-ring.mp4 Detecting Duplicates with SHA-3: A Data Integrity Tool: 2.15-sha3-dupe-detector.mp4 Incident Response: 2.16-incident-response.mp4 Compliance: 2.17-compliance.mp4 Lesson 3: Concurrency with Rust Core Concepts in Concurrency: 2.20-core-concepts-concurrency.mp4 Dining Philosophers: 2.21-dining-philosopher.mp4 Web Crawl Wikipedia with Rayon: 2.22-web-crawl-wikipedia-rayon.mp4 Intelligent Chatbot with Tokio: 2.23-tokio-chatbot.mp4 Multi-threaded deduplication with Rust: 2.24-data-eng-with-rust-dedupe.mp4 Energy Efficiency Python vs Rust: 2.25-energy-efficiency-python-rust.mp4 Concurrency Stress test with a GPU: 2.26-building-cuda-enabled-stress-test-with-rust-pytorch.mp4 Host Efficiency Serverless Optimization problem: 2.27-host-efficiency-optimization-problem.mp4 Section 3: Rust Data Engineering Libraries and Tools Lesson 1: Using Rust to Manage Data, Files and Network Storage Process CSV files in Rust: 2.31-process-csv-rust.mp4 Using Cargo Lambda with Rust: 2.33-cargo-lambda-rust.mp4 List files on AWS EFS with Rust: 2.34-rust-efs-lister.mp4 Use AWS S3 Storage: 2.35-use-s3-storage.mp4 Use AWS S3 Storage from Rust: 2.36-use-rust-for-s3-storage.mp4 Write encrypted data to tables or Parquet files: 2.37-Write-encrypted-data-to-tables-or-Parquet-files.mp4 Lesson 2: DataFrames with Rust, Python and Notebooks What is Colab?: 2.40-what-is-colab.mp4 Using Bard to enhance notebook development: 2.41-using-bard-to-enhance-productivity.mp4 Exploring Life Expectency in a Notebook: 2.42-life-expectancy-eda.mp4 Load a DataFrame with sensitive data: 2.43-Load-a-DataFrame-with-sensitive-information.mp4 Using MLFlow with Databricks Notebooks: 2.44-mlops-mlflow-tracking.mp4 End to End ML with MLFlow and Databricks: 2.45-end-to-end-ml-with-mlflow-and-databricks.mp4 Comparing DataFrame Libraries between Rust and Python: 2.46-comparing-dataframe-libs-rust-python.mp4 Lesson 3: Data Engineering Libraries and Tools with Rust Parquet file writing and reading with Rust: 2.50-parquet-read-write-rust.mp4 Arrow & Parquet in Rust: 2.51-arrow-parquet-rust.mp4 Serverless functions with Rust and AWS Lambda: 2.52-serverless-rust-aws-lambda.mp4 Polars library overview: 2.53-polars-library-overview.mp4 Building RESTful APIs with Rocket: 2.54-building-restful-apis-rocket.mp4 Utilizing Async Rust in Web Development: 2.55-utilizing-async-rust-web-development.mp4 Applying Data Cleaning Techniques with Rust: 2.56-applying-data-cleaning-rust.mp4 Deploying Rust Applications in a Kubernetes Environment: 2.57-deploying-rust-apps-kubernetes.mp4 Section 4: Designing Data Processing Systems in Rust Lesson 1: Getting Started with Rust Data Pipelines (Including ETL) Jack and the Beanstalk Data Pipelines: 4.1-jack-beanstalk-building-data-pipelines.mp4 Open Source Data Engineering - Pros and Cons: 4.2-open-source-de-pro-con.mp4 Core Components of Data Engineering Pipelines: 4.3-core-components-data-engineering-pipelines.mp4 Rust AWS Step Functions Pipeline: 4.4-rust-aws-step-functions.mp4 Rust AWS Lambda Async S3 Size Calculator: 4.5-rust-async-s3-size-calculator-lambda.mp4 What is Distroless: 4.6-what-is-distroless.mp4 Demo Deploying Rust Microservices on GCP: 4.7-demo-build-deploy-rust-microservice-cloud-run.mp4 Lesson 2: Using Rust and Python for LLMs, ONNX, Hugging Face, and PyTorch Pipelines Introduction to Hugging Face Hub: 4.10-intro-hugging-face-hub.mp4 Rust PyTorch Pre-trained Model Ecosystem: 4.11-rust-pytorch-pretrained-models-ecosystem.mp4 Rust GPU Hugging Face Translator: 4.12-rust-gpu-hugging-face-translator.mp4 Rust PyTorch High-Performance Options: 4.13-high-performance-pytorch-rust-demo.mp4 Rust CUDA PyTorch Stress Test: 4.14-building-cuda-enabled-stress-test-with-rust-pytorch.mp4 EFS ONNX Rust Inference with AWS Lambda: 4.15-efs-onnx-lambda-rust-inference-mlops.mp4 Theory behind model fine-tuning: 4.16-intro-fine-tuning-theory.mp4 Doing Fine Tuning: 4.17-doing-fine-tuning.mp4 Lesson 3: Building SQL Solutions with Rust, Generative AI and Cloud Selecting the correct database on GCP: 4.20-gcp-optimize-d...

Abstract: Live Coding Rust Candle from Hugging Face Learning LLMOps This video series covers live coding Rust Candle from Hugging Face for LLMOps Lessons Covered Include: 1.0 Building binaries for Phi using AWS CodeBuild and Rust Candle Learning Objectives Run your own Rust LLMs Additional Popular Resources 52 Weeks of AWS-The Complete Series Microsoft Azure Fundamentals (AZ-900) Certification Rust Bootcamp Python Bootcamp Google Professional Machine Learning Engineer Course 2023 (Rough Draft) Rust Data Engineering Building with the GitHub EcoSystem: Copilot, CodeSpaces, and GitHub Actions Microsoft Azure Fundamentals (AZ-900) Certification Google Professional Cloud Architect Certification Course 2023 (Rough Draft) AWS Solutions Architect Professional (SAP-C02) 2023.

Abstract: This introductory lesson will walk you through everything you need to know to quickly get started with Azure ML Studio using AutoML. After creating the necessary resources, you'll walk through the steps needed to create a regression job using a dataset. Next you'll create a compute cluster and review the different options available and finally, you'll analyze the results after the best model is selected.

Abstract: This video series covers live coding with TensorFlow and the Rust language.

Abstract: Google Professional Machine Learning Engineer Course 2023 (Rough Draft) Certification Exam Guide Welcome to the Google Professional Machine Learning Engineer Course! This course is designed to help you prepare for the Google Professional Machine Learning Engineer certification exam. Learning Objectives Develop a deep understanding of Google Cloud technologies and various ML models and techniques to design, build, and productionize machine learning solutions that address specific business challenges while adhering to responsible AI practices. Collaborate effectively with cross-functional teams, including application developers, data engineers, and data governance professionals, to ensure the long-term success of ML models throughout their development, deployment, and maintenance. Master the skills required to design, implement, and manage ML architectures, data pipelines, and metric interpretations, as well as optimize model performance through training, retraining, deploying, scheduling, monitoring, and refining models in scalable and efficient ways. Learn how to use the Google Cloud Platform (GCP) to build and deploy ML models, including how to use GCP services such as BigQuery, Cloud Storage, Cloud AI Platform, and Cloud Functions to build and deploy ML models. Who Should Take This Course? Data scientists Data engineers Machine learning engineers Software engineers Data analysts Data architects Business analysts Anyone interested in learning about machine learning and Google Cloud Platform Course One: Framing ML Problems Course Two: Architecting ML solutions Course Three: Designing data preparation and processing systems Course Four: Developing ML models Course Five: Automating and orchestrating ML pipelines Course Six: Monitoring, optimizing, and maintaining ML solutions Additional Popular Resources Pytest Master Class AWS Solutions Architect Professional Course Github Actions and GitOps in One Hour Video Course Jenkins CI/CD and Github in One Hour Video Course AWS Certified Cloud Practitioner Video Course Advanced Testing with Pytest Video Course AWS Solutions Architect Certification In ONE HOUR Python for DevOps Master Class 2022: CI/CD, Github Actions, Containers, and Microservices MLOPs Foundations: Chapter 2 Walkthrough of Practical MLOps Learn Docker containers in One Hour Video Course Introduction to MLOps Walkthrough AZ-900 (Azure Fundamentals) Quick reference guide 52 Weeks of AWS Episode 8: Infrastructure as Code with CDK and AWS Lambda Learn GCP Cloud Functions in One Hour Video Course Python Devops in TWO HOURS! MLOps Platforms From Zero: DatMLOps Platforms From Zero: Databricks, MLFlow/MLRun/SKLearn AWS Machine Learning Certification In ONE HOUR Fast, documented Machine Learning APIs with FastAPI Zero to One: AWS Lambda with SAM and Python in One Hour AWS Storage Solutions 2022: EBS/S3/EFS/Glacier Python Bootcamp for Data Testing In Python book Minimal Python book Practical MLOps book Python for DevOps-Playlist.

Abstract: Getting Started with the Zig Programming Language Learning the Language During Live Coding This video series covers the basics of the Zig programming language through live coding, allowing viewers to learn the language iteratively as it is being used. Lessons Covered 1.0 Getting Started Learning Objectives Learn the fundamentals of the Zig programming language Learn to build solutions with Zig Learn the key syntax and features of Zig Additional Popular Resources Pytest Master Class AWS Solutions Architect Professional Course Github Actions and GitOps in One Hour Video Course Jenkins CI/CD and Github in One Hour Video Course AWS Certified Cloud Practitioner Video Course Advanced Testing with Pytest Video Course AWS Solutions Architect Certification In ONE HOUR Python for DevOps Master Class 2022: CI/CD, Github Actions, Containers, and Microservices MLOPs Foundations: Chapter 2 Walkthrough of Practical MLOps Learn Docker containers in One Hour Video Course Introduction to MLOps Walkthrough AZ-900 (Azure Fundamentals) Quick reference guide 52 Weeks of AWS Episode 8: Infrastructure as Code with CDK and AWS Lambda Learn GCP Cloud Functions in One Hour Video Course Python Devops in TWO HOURS! MLOps Platforms From Zero: DatMLOps Platforms From Zero: Databricks, MLFlow/MLRun/SKLearn AWS Machine Learning Certification In ONE HOUR Fast, documented Machine Learning APIs with FastAPI Zero to One: AWS Lambda with SAM and Python in One Hour AWS Storage Solutions 2022: EBS/S3/EFS/Glacier Python Bootcamp for Data Testing In Python book Minimal Python book Practical MLOps book Python for DevOps-Playlist.

Abstract: Hands-on Python for MLOps Build powerful APIs, tools, and scripts with Python Use Python to create APIs and automation scripts for machine learning operations. Learn to leverage APIs, build CLI tools, and develop web services. If you are new to Machine Learning Operations or want to know more about the foundations of Python for MLOps, this course will walk you through the basics. In this course, you'll learn how to use Python for automation, APIs, and building useful command-line interfaces. We'll cover consuming APIs, creating CLI tools, packaging projects, and building web services to expose machine learning models. The course includes hands-on examples and projects so you can apply what you learn right away. By the end of the course, you'll have practical skills to automate workflows and develop interfaces for machine learning. This course includes GitHub repositories you can reference: Python CLI Examples Basic Python CLI Web API with Python and Hugging Face models Learn objectives Consume and use APIs and SDKs in Python Create command-line programs for automation Develop web services and APIs with Python frameworks Package Python projects for distribution Apply skills to build useful interfaces for ML models Lesson 1: Working with APIs in Python Lesson Outline Overview of APIs and SDKs Using the Requests Library Consuming REST APIs Python SDKs like NumPy and SciPy Project: Building a Python script using APIs Lesson 2: Building Command-line Interfaces Lesson Outline Intro to automation with CLI tools Parsing arguments and options Organizing code into modules Python packaging for distribution Project: CLI tool for machine learning Lesson 3: Developing Web APIs Lesson Outline REST API concepts Web frameworks like Flask and FastAPI Building an API with Flask Developing APIs with FastAPI OpenAPI specs and documentation Project: Web API for movie recommendations About your instructor Alfredo Deza has over a decade of experience as a Software Engineer doing DevOps, automation, and scalable system architecture. Before getting into technology he participated in the 2004 Olympic Games and was the first-ever World Champion in High Jump representing Peru. He currently works in Developer Relations at Microsoft and is an Adjunct Professor at Duke University. This solid background in technology and teaching, including his past experience as a Linux system administrator is seen throughout this course, where you will get a first-hand experience with high-level knowledge and practical examples. Resources Pytest Master Class Practical MLOps book.

Abstract: Live Coding in Rust: Unleash the Power of Systems Programming Delve deep into the Rust ecosystem with this groundbreaking live coding series, focused on building robust and performant software, including cutting-edge topics like LLMOps and machine learning with Hugging Face's Candle library. What You'll Get Live coding sessions with expert commentary Weekly updates and continually expanding! High-impact projects and examples Exclusive discussions on Rust best practices Topics covering LLMOps, machine learning, and more A focus on performance, safety, and systems programming Course Outline (Future and Current Content) Rust Fundamentals Introduction to Rust Understanding Ownership & Borrowing Error Handling in Rust LLMOps in Rust Live Coding Rust: LLMOps in Rust--Exploring Hugging Face Candle Building Custom LLMOps Libraries Rust and LLVM: A Match Made in Heaven Machine Learning in Rust Implementing ML Algorithms in Rust Using Hugging Face Candle for NLP Deploying Rust ML Models in Production Systems Programming Writing File Systems in Rust Creating Web Servers with Actix Asynchronous Programming with async/await Rust in DevOps Automating CI/CD Pipelines with Rust Rust for Cloud Operations Rust in Containerized Environments Topics Covered LLMOps : The intersection of LLVM and DevOps Machine Learning : Building and deploying models in Rust Systems Programming : Low-level operations made easier and safer DevOps : Leverage Rust for automating operations tasks Additional Popular Resources Assimilate OpenAI 52 Weeks of AWS-The Complete Series Microsoft Azure Fundamentals (AZ-900) Certification Rust Bootcamp Python Bootcamp Google Professional Machine Learning Engineer Course 2023 (Rough Draft) Rust Data Engineering Building with the GitHub EcoSystem: Copilot, CodeSpaces, and GitHub Actions Microsoft Azure Fundamentals (AZ-900) Certification Google Professional Cloud Architect Certification Course 2023 (Rough Draft) AWS Solutions Architect Professional (SAP-C02) 2023.

Abstract: Rust LLMOps: A Rough Draft Course (Updated JIT) Learning During Live Coding This video series is a rough draft course being updated JIT (Just in Time) and covers the Rust language through live coding. We offer an iterative approach to understand and master the language, particularly focusing on its application in MLOps. Lessons Covered Include: Rust Hugging Face Candle Description This week, you will delve into the powerful combination of Rust with Candle, a minimalist ML framework, and explore how they can be used with Hugging Face's popular transformer models. You will apply these concepts by working on a series of hands-on labs that guide you through building, training, and deploying machine learning models using Rust, Candle, and Hugging Face. Learning Objectives By the end of this lesson, you will be able to: - Implement Candle in Rust to create and train machine learning models. - Utilize the various features of Candle, including syntax, GPU efficiency, and browser deployment. - Integrate Hugging Face transformers with Rust for natural language processing tasks. - Apply the principles of Rust and Candle to build real-world machine learning applications. - Evaluate the performance and scalability of Rust-based ML solutions using Candle and Hugging Face. Lesson Structure Original 4.1 Candle: A Minimalist ML Framework for Rust 4.2 Using GitHub Codespaces for GPU Inference with Rust Candle 4.3 VSCode Remote SSH development AWS Accelerated Compute 4.4 Building Hello World Candle 4.5 Exploring StarCoder: A State-of-the-Art LLM for Code 4.6 Using Whisper with Candle to Transcribe 4.7 Exploring Remote Dev Architectures on AWS.

Abstract: Rust For DevOps Learn how to use and apply DevOps principles using Rust This is an early release of the course. In this course, you will learn the basics of DevOps using the Rust programming language. DevOps helps you build, test, and deploy code faster while creating a robust foundation for your projects and teams. First, we will talk about what DevOps is. You will learn things like automation, monitoring, and working together as a team. Then, we will see how to use DevOps ideas in Rust code. You will learn about version control, testing, containers, and deploying Rust programs. Although this course explains the foundations of DevOps and using Rust which is ideal for beginners, experienced Rust developers will find solid advice in how to apply DevOps best practices to existing applications. Learning Objectives The core principles and practices of DevOps How to apply DevOps concepts to build, package, and deploy applications with Rust The basics of containerization and how to leverage containers for DevOps with Rust Course Content This course is divided into 4 weeks with 3 lessons that contain about 6 videos each. Each video is about 5 minutes long and contains a hands-on demonstration of the concepts covered in the lesson. The course is designed to be taken in order, but you can jump to any lesson you want to learn more about. Week 1: Introduction to DevOps Principles Introduction to DevOps Principles DevOps Considerations for Applications Basics of Containerization Week 2: Implementing monitoring and logging strategies Introduction to monitoring and logging strategies Tools and Services for monitoring and logging Logging with Rust About your instructor Alfredo Deza has over a decade of experience as a Software Engineer doing DevOps, automation, and scalable system architecture. Before getting into technology he participated in the 2004 Olympic Games and was the first-ever World Champion in High Jump representing Peru. He currently works in Developer Relations at Microsoft and is an Adjunct Professor at Duke University teaching Machine Learning, Cloud Computing, Data Engineering, Python, and Rust. With Alfredo's guidance, you will gain the knowledge and skills to build robust applications and infrastructure with the Rust programming language and for Rust applications. Resources Rust Bootcamp Deploy Rust on Azure Functions DevOps command-line tools in Python and Rust Switching to Rust from Python First Steps with Rust Learning Path.

Abstract: GitHub Enterprise - Certified Learning the GitHub Enterprise Certification Material This course covers all seven domains of the the GitHub Enterprise Certification Lessons Covered Include: Domain 1: Support GitHub Enterprise for users and key stakeholders Distinguish admin tasks vs needing GitHub Support Generate support bundles and diagnostics Identify underutilized features, integrations, active teams/repos Recommend standards for workflows, branching, code review, automation Explain tooling ecosystem and CI/CD strategy Recommend workflows and tooling to teams Use GitHub APIs to extend admin capabilities Find Marketplace assets for specific needs Contrast GitHub Apps vs actions Domain 2: Manage user identities and GitHub authentication Implications of enabling SAML SSO for orgs/enterprise Steps to enable and enforce SAML SSO Require two-factor authentication Supported identity providers GitHub identity management and authorization Authentication and authorization model Supported SCIM providers How SCIM and team sync works Domain 3: Describe deployment, distribution, licensing Capabilities of GHES, GHEC, GHAE Billing models including licenses, Actions, Packages License usage statistics for orgs, enterprise, machine accounts Consumption of metered products Domain 4: Manage access and permissions Enterprise permissions and policies Organization permissions Team permissions Repository permissions Domain 5: Enable secure development and compliance How GitHub supports enterprise security Scrubbing sensitive data Choosing policies for control required Impacts of policies Organization and enterprise policies Use audit log APIs to explain missing assets Security features of GitHub repositories Create security response plan SSH keys and deploy keys Domain 6: Manage GitHub Actions Distribute actions and workflows in enterprise Manage runners Manage encrypted secrets Domain 7: Manage GitHub Packages Supported Packages Accessing, writing, and sharing Packages Using Packages in workflows Contrasting Packages and releases Learning Objectives Manage user access and permissions across GitHub Enterprise environments through authentication, organizations, teams, and repositories. Establish secure development practices and compliance controls with GitHub features. Distribute and manage GitHub Actions across the enterprise. Additional Popular Resources 52 Weeks of AWS-The Complete Series Microsoft Azure Fundamentals (AZ-900) Certification Rust Bootcamp Python Bootcamp Google Professional Machine Learning Engineer Course 2023 (Rough Draft) Rust Data Engineering Building with the GitHub EcoSystem: Copilot, CodeSpaces, and GitHub Actions Microsoft Azure Fundamentals (AZ-900) Certification Google Professional Cloud Architect Certification Course 2023 (Rough Draft) AWS Solutions Architect Professional (SAP-C02) 2023.

Abstract: Assimilate ONNX Learning about ML Model portability During Live Coding This video series covers live coding the Language iteratively thus learning the language. Lessons Covered Include: 1.0 Getting started with ONNX. In this live coding session I was able to get both resnet and ONNX working in Rust with Sonos/Trac. Learning Objectives Learn to work with ONNX models Learning to build solutions with ONNX models in Rust Learning the key syntax and features ONNX models Additional Popular Resources Pytest Master Class AWS Solutions Architect Professional Course Github Actions and GitOps in One Hour Video Course Jenkins CI/CD and Github in One Hour Video Course AWS Certified Cloud Practitioner Video Course Advanced Testing with Pytest Video Course AWS Solutions Architect Certification In ONE HOUR Python for DevOps Master Class 2022: CI/CD, Github Actions, Containers, and Microservices MLOPs Foundations: Chapter 2 Walkthrough of Practical MLOps Learn Docker containers in One Hour Video Course Introduction to MLOps Walkthrough AZ-900 (Azure Fundamentals) Quick reference guide 52 Weeks of AWS Episode 8: Infrastructure as Code with CDK and AWS Lambda Learn GCP Cloud Functions in One Hour Video Course Python Devops in TWO HOURS! MLOps Platforms From Zero: DatMLOps Platforms From Zero: Databricks, MLFlow/MLRun/SKLearn AWS Machine Learning Certification In ONE HOUR Fast, documented Machine Learning APIs with FastAPI Zero to One: AWS Lambda with SAM and Python in One Hour AWS Storage Solutions 2022: EBS/S3/EFS/Glacier Python Bootcamp for Data Testing In Python book Minimal Python book Practical MLOps book Python for DevOps-Playlist.

Abstract: Rust Bootcamp Course Overview Welcome to our comprehensive Rust programming course! In this immersive learning experience, we will guide you through every aspect of Rust, empowering you to become a proficient developer. This course is ideal for beginners wanting to become proficient in Rust or from existing developers coming from languages like Python or JavaScript that want to learn Rust fundamentals. Get started by setting up your preferred text editor and installing Rust. We'll ensure that you have all the necessary tools to create a productive coding environment. By leveraging the capabilities of Visual Studio Code and enabling the Rust Analyzer, you'll unlock a feature-rich ecosystem that enhances your development workflow. Experience the game-changing potential of GitHub Copilot, an AI-powered assistant. Sign up for GitHub Copilot and witness its transformative impact on your coding experience. We'll guide you through the installation process and show you how to leverage its intelligent suggestions for accelerated programming. Master concepts and fundamentals like control flow, variable assignment, and immutability. Learn the intricacies of loops, conditional statements, and effective error handling techniques. With Rust's borrowing concept at your disposal, you'll develop secure and high-performance code. Build upon your foundation with advanced topics like structs, string manipulation, and working with vectors and enums. We'll explore real-world library development using Cargo, Rust's robust package manager. Effective documentation and debugging techniques will become second nature as you optimize your code. Organize your code effectively with modules, extending functionality and enhancing reusability. Dive into comprehensive testing methodologies to ensure the reliability and correctness of your programs. From writing tests for your code to handling private components, you'll gain expertise in developing robust software projects. Our immersive learning experience includes hands-on examples, interactive exercises, and practical projects. Rust's performance, safety, and expressiveness will be at your fingertips as you embrace its immense potential. Join us on this rewarding journey to unlock the power of Rust programming and propel your career forward. Enroll now and gain the skills, knowledge, and expertise to thrive in the world of Rust. Whether you're a beginner, coding novice, or an experienced developer seeking new horizons, our course is your gateway to writing elegant, reliable, and high-performance code. Embrace the possibilities of Rust and embark on a rewarding programming journey today with one of the most loved programming languages. Learning Objectives Gain a solid understanding of Rust's core concepts, including variable assignment, control flow, and immutability, to write efficient and reliable code. Master the utilization of powerful development tools such as Visual Studio Code and the Rust Analyzer, enhancing productivity and enabling seamless coding experiences. Explore GitHub Copilot and learn how to leverage its AI-driven suggestions to accelerate programming and boost code quality. Develop proficiency in working with advanced Rust features, such as structs, enums, and string manipulation, to build robust and flexible applications. Discover the intricacies of module organization and extend functionality through effective code structuring, promoting code reusability and maintainability. Acquire comprehensive testing skills, including writing tests for code components and handling private code, ensuring the reliability and correctness of Rust programs. Gain hands-on experience in real-world library development using Cargo, Rust's package manager, and become proficient in documenting code and debugging techniques. These compelling learning objectives will guide you through the course, helping you acquire essential skills and knowledge to become a confident Rust developer. Course Content This course is divided into 4 weeks with 3 lessons that contain about 6 videos each. Each video is about 5 minutes long and contains a hands-on demonstration of the concepts covered in the lesson. The course is designed to be taken in order, but you can jump to any lesson you want to learn more about. Week 1: Setting up your development environment Reference GitHub Repository Setting up your text editor Using GitHub Copilot with Rust Introduction to GitHub Codespaces for Rust Week 2: Rust fundamentals Reference GitHub Repository Introduction to Rust Loops and Control flow Function basics Week 3: Structs, Types, and Enums Reference GitHub Repository Using structured data Exploring strings and vectors Working with Enum and Variants Week 4: Applying Rust Reference GitHub Repository Building a real-world library Extending functionality with modules Testing Rust code About your instructor Alfredo Deza has over a decade of experience as a Software Engineer doing DevOps, automation, and scalable system architecture. Before getting into technology he participated in the 2004 Olympic Games and was the first-ever World Champion in High Jump representing Peru. He currently works in Developer Relations at Microsoft and is an Adjunct Professor at Duke University teaching Machine Learning, Cloud Computing, Data Engineering, Python, and Rust. With Alfredo's guidance, you will gain the knowledge and skills to build with the Rust programming language. Resources Deploy Rust on Azure Functions DevOps command-line tools in Python and Rust Switching to Rust from Python First Steps with Rust Learning Path.

Abstract: Rust for Pythonistas What you'll learn-and how you can apply it A multi-hour jam session where you will learn to code in Rust. * Write efficient and safe Rust code * Leverage Rust libraries and tools in your projects * Combine Rust with Python for improved application performance Additional Popular Resources 52 Weeks of AWS-The Complete Series Microsoft Azure Fundamentals (AZ-900) Certification Rust Bootcamp Python Bootcamp Google Professional Machine Learning Engineer Course 2023 (Rough Draft) Rust Data Engineering Building with the GitHub EcoSystem: Copilot, CodeSpaces, and GitHub Actions Microsoft Azure Fundamentals (AZ-900) Certification Google Professional Cloud Architect Certification Course 2023 (Rough Draft) AWS Solutions Architect Professional (SAP-C02) 2023.

Abstract: Microsoft Azure AI Fundamentals (AI-900) Certification Pass the AI Fundamentals certification This early release (ongoing) course will give you everything you need to pass the Azure AI Fundamentals (AI-900) certification. This is the foundational certification for doing Machine Learning and Data Science using Azure and its specialized services. The course will go into every content that is relevant to pass the certification including how to create a study guide and strategy to pass the exam. This course includes and covers all of the author's study notes so that you can use them in addition to your own notes . Course overview This course contains a complete walkthrough over all of the content relevant for the AI-900 certification with a summary and specialized notes after each sections, following the same content that is available as Learning Paths. Aside from the video content, create your own study notes and strategy using the example reference guide Course Content Get Started with AI Get Started with AI Study notes Get Started with Artificial Intelligence on Azure About your instructor Alfredo Deza has over a decade of experience as a Software Engineer doing DevOps, automation, and scalable system architecture. Before getting into technology he participated in the 2004 Olympic Games and was the first-ever World Champion in High Jump representing Peru. He currently works in Developer Relations at Microsoft and is an Adjunct Professor at Duke University. This solid background in technology and teaching, including his experience studying and passing the AI-900 is seen throughout this course, where you will get a first-hand experience with study notes, example repositories, and a solid study strategy. Resources Pytest Master Class Practical MLOps book.

Abstract: Google Professional Cloud Architect Certification Course 2023 (Rough Draft) Welcome to the Google Professional Cloud Architect Certification Course! This course is designed to help you prepare for the Google Cloud Certified Professional Cloud Architect certification exam. As a professional cloud architect, you will be responsible for designing, developing, and managing secure, scalable, and highly available cloud solutions using Google Cloud Platform. Course Overview Course One: Designing and planning a cloud solution architecture 1.0 Rough Draft Introduction Introduction to Google Professional Architect Course Onboarding to GCP Cloud Designing a solution infrastructure that meets business requirements Designing a solution infrastructure that meets technical requirements Designing network, storage, and compute resources Using Google Cloud Functions Running Minikube Running Minikube with FastAPI for Kubernetes development Getting Started with Google Cloud Run Load testing with locust Design technical solutions for evolution and migration SRE Mindset Conclusion and Next Steps Course Two: Managing and provisioning a solution infrastructure Introduction to Google Professional Architect Course Two Exploring Network Topologies in GCP: A Demonstration Embracing Hybrid Networking in Google Cloud Platform Overview of GCP Storage Demo of using the SDK and CLI to manage storage Data lifecycle management Data security Demo of building Rust deduplication finder Compute volatility configuration (preemptible vs. standard) Demo Build and Deploy Rust Actix Microservice Cloud Run Container orchestration with GKE Demo of extending Google Cloud Functions Conclusion Course Two Course Three: Designing for Security and Compliance Introduction to Google Professional Architect Course Three Mastering Identity and Access Management in GCP Data Security Best Practices for GCP Secrets Manager API: A Hands-on Guide Exploring a data poisoning attack Meeting Industry Certification Requirements in GCP Auditing GCP Security with Log Analysis Creating Custom Log Dashboards for GCP Security Monitoring Scanning Web Applications with GCP's Cloud Web Security Scanner Analyzing GCP Logs with BigQuery GCP Security: Conclusion and Next Steps Course Four: Designing and optimizing technical and business processes Introduction to Google Professional Architect Course Four Continuous Delivery: An Introduction Hands-on with Continuous Integration in GCP Troubleshooting Techniques: The Five Whys Implementing Simple GitHub Actions for CI/CD The Three Most Important Files in a Python Project Utilizing Trello for Effective Project Management Leveraging Spreadsheets for Project Management Exploring the MLOps Maturity Model Building Bespoke Systems for Core Business Functions Data Science on Windows: Virtualenv and Pip Site Reliability Engineering (SRE) in GCP Performing Load Testing in GCP: A Demo Course Five: Managing and optimizing cloud solution Implementation Introduction to Google Professional Architect Course Five Makefiles Made Easy: A Comprehensive Guide Configuring Your Bashrc for an Optimal Environment Implementing Continuous Integration for Rust with GitHub Actions Unit Testing in Rust: A Hands-on Demonstration Supercharging Rust Development with Copilot Setting Up a GCP Workstation for Python Development Mastering Google Cloud Shell: A Practical Guide Streamline Your Development with Google Cloud Editor Getting Started with the Google Cloud CLI SDK Unleashing the Power of Google Cloud's 'gcloud' Command-Line Tool Deploying Rust Applications to App Engine: A Step-by Course Six: Ensuring solution and operations reliability Introduction to Google Professional Architect Course Six Monitoring and Logging Rust App Engine Applications: A Demo Uncovering the Advantages of Cloud Developer Workspaces Operationalizing Microservices in the Cloud Introducing GitHub Codespaces for Streamlined Development Compiling Python Projects in GitHub Codespaces Mastering Continuous Integration in GCP Course Wrap-up and Future Steps in Your GCP Journey Learning Objectives Design and optimize cloud solutions Analyze and optimize technical and business processes Manage and provision cloud infrastructure Ensure solution and operations reliability By the end of this course, you will have the knowledge and skills needed to pass the Google Cloud Certified Professional Cloud Architect exam and to effectively design and manage cloud solutions using Google Cloud Platform. Additional Popular Resources Pytest Master Class AWS Solutions Architect Professional Course Github Actions and GitOps in One Hour Video Course Jenkins CI/CD and Github in One Hour Video Course AWS Certified Cloud Practitioner Video Course Advanced Testing with Pytest Video Course AWS Solutions Architect Certification In ONE HOUR Python for DevOps Master Class 2022: CI/CD, Github Actions, Containers, and Microservices MLOPs Foundations: Chapter 2 Walkthrough of Practical MLOps Learn Docker containers in One Hour Video Course Introduction to MLOps Walkthrough AZ-900 (Azure Fundamentals) Quick reference guide 52 Weeks of AWS Episode 8: Infrastructure as Code with CDK and AWS Lambda Learn GCP Cloud Functions in One Hour Video Course Python Devops in TWO HOURS! MLOps Platforms From Zero: DatMLOps Platforms From Zero: Databricks, MLFlow/MLRun/SKLearn AWS Machine Learning Certification In ONE HOUR Fast, documented Machine Learning APIs with FastAPI Zero to One: AWS Lambda with SAM and Python in One Hour AWS Storage Solutions 2022: EBS/S3/EFS/Glacier Python Bootcamp for Data Testing In Python book Minimal Python book Practical MLOps book Python for DevOps-Playlist.

Abstract: Deploy Rust on Azure Functions Learn how to deploy Rust functions to Azure Functions with this comprehensive video course. You will start by creating a simple HTTP-triggered function, and then use GitHub Actions to automate your deployment. Deploying Rust in Azure can be frustrating because there is no first-class support yet for it. You will learn what are some of the caveats and what are some strategies you can use to properly deploy a very basic Rust HTTP API. Course Overview We will start by covering the basics of Azure Functions and Rust, and then dive into more advanced topics such as configuring your function's environment, application settings, and using external crates. By the end of this course, you will have a working knowledge of how to deploy and manage your Rust functions on Azure Functions using automation to quickly re-deploy or update your function. Course Content Introduction to Azure Functions - Learn the basics of Azure Functions and how they work with Rust. Creating Your First Azure Function in Rust - Create a simple HTTP-triggered function in Rust and deploy it to Azure Functions. Configuring Your Function Environment - Learn how to use environment variables to configure your function's behavior. Use GitHub Actions for automation About your instructor Alfredo Deza has over a decade of experience as a Software Engineer doing DevOps, automation, and scalable system architecture. Before getting into technology he participated in the 2004 Olympic Games and was the first-ever World Champion in High Jump representing Peru. He currently works in Developer Relations at Microsoft and is an Adjunct Professor at Duke University. With Alfredo's guidance, you will gain the knowledge and skills needed to deploy and manage your Rust functions on Azure Functions. Resources Deploying containers to Azure Practical MLOps book Automated Azure Resource Cleanup Azure Fundamentals Certification.

Abstract: Professional Data Engineer Certification Course Section 1: Designing Data Processing Systems 1.1 Storage technology selection 1.2 Data pipeline design 1.3 Data processing solution design 1.4 Data warehousing & processing migration Section 2: Building & Operationalizing Data Processing Systems 2.1 Storage system implementation 2.2 Pipeline building & operationalization 2.3 Processing infrastructure implementation Section 3: Operationalizing ML Models 3.1 Pre-built ML models as a service 3.2 ML pipeline deployment 3.3 Training & serving infrastructure selection 3.4 ML model measurement, monitoring & troubleshooting Section 4: Ensuring Solution Quality 4.1 Security & compliance design 4.2 Scalability & efficiency assurance 4.3 Reliability & fidelity assurance 4.4 Flexibility & portability assurance Learning Objectives Learn to design, build, and operationalize data processing systems that meet business requirements, system requirements, and industry best practices. Learning to build scalable, efficient, and secure solutions with Google Cloud technologies to manage and analyze data at scale. Learning the key syntax and features of the SQL language for handling, querying, and performing operations on structured data in Google Cloud. Additional Popular Resources Pytest Master Class AWS Solutions Architect Professional Course Github Actions and GitOps in One Hour Video Course Jenkins CI/CD and Github in One Hour Video Course AWS Certified Cloud Practitioner Video Course Advanced Testing with Pytest Video Course AWS Solutions Architect Certification In ONE HOUR Python for DevOps Master Class 2022: CI/CD, Github Actions, Containers, and Microservices MLOPs Foundations: Chapter 2 Walkthrough of Practical MLOps Learn Docker containers in One Hour Video Course Introduction to MLOps Walkthrough AZ-900 (Azure Fundamentals) Quick reference guide 52 Weeks of AWS Episode 8: Infrastructure as Code with CDK and AWS Lambda Learn GCP Cloud Functions in One Hour Video Course Python Devops in TWO HOURS! MLOps Platforms From Zero: DatMLOps Platforms From Zero: Databricks, MLFlow/MLRun/SKLearn AWS Machine Learning Certification In ONE HOUR Fast, documented Machine Learning APIs with FastAPI Zero to One: AWS Lambda with SAM and Python in One Hour AWS Storage Solutions 2022: EBS/S3/EFS/Glacier Python Bootcamp for Data Testing In Python book Minimal Python book Practical MLOps book Python for DevOps-Playlist.

Abstract: Introduction to MLflow for MLOps Learn how to use MLflow for managing the machine learning lifecycle. Track experiments, package models, and deploy to production. In this course you'll learn how to use MLflow - an open source platform for managing the machine learning lifecycle. You'll learn how to: Install MLflow and explore its components like the UI, tracking, and model packaging Log metrics, parameters, and artifacts to track ML experiments Create reproducible ML projects with MLflow for repeatable model training Package models and dependencies for deployment and serving Use model registries to version, stage, and deploy models Deploy models to tools like Azure ML and SageMaker This course includes hands-on exercises, projects, and real-world examples so you can apply your new MLflow skills immediately. Use the reference repository for MLFlow examples and projects: Example MLFlow Projects Learning objectives Install and configure MLflow Use the tracking UI and APIs Log metrics, parameters, tags, and artifacts Create reproducible ML projects Version, stage, and deploy models with registries Deploy models to Azure ML, SageMaker, etc Lesson 1: Introduction to MLflow Lesson Outline Overview of MLflow components Installation and configuration Tracking experiments with UI, Python, R APIs Logging metrics, params, tags, artifacts Lesson 2: MLflow Projects Lesson Outline Motivation for reproducible ML projects Creating project directories Running projects locally or on Git Customizing execution environments Lesson 3: MLflow Models Lesson Outline Packaging models and dependencies Model versioning with registries Staging and promoting model stages Deploying models to services About your instructor Alfredo Deza has over a decade of experience as a Software Engineer doing DevOps, automation, and scalable system architecture. Before getting into technology he participated in the 2004 Olympic Games and was the first-ever World Champion in High Jump representing Peru. He currently works in Developer Relations at Microsoft and is an Adjunct Professor at Duke University teaching Machine Learning, Cloud Computing, Data Engineering, Python, and Rust. With Alfredo's guidance, you will gain the knowledge and skills to work with MLFlow and apply it to MLOps tasks. Resources Pytest Master Class Practical MLOps book.

Abstract: Scripting with Python and SQL for Data Engineering Learn Python data structures, web scraping, SQL, and MySQL from the ground up. Master essential skills for collecting, storing, and working with data. In this hands-on course for beginners, you'll learn how to: Store and manipulate data using Python lists, dictionaries, JSON Write reusable scripts to process data Connect Python to databases like SQLite and MySQL Query, import, and export data with SQL Scrape and parse websites using Beautiful Soup and Scrapy Persist scraped data to databases and files You'll use the following example repositories to practice: Mapping data in Python Python Scripting Scrapping Basics Key topics include: Mapping, iterating, and persisting data structures Creating modules, scripts, and workflows in Python SQL essentials - queries, statements, aggregations Setting up connections from Python to SQLite and MySQL Scraping data locally and at scale with spiders Storing scraped data to optimize pipelines You'll build your data wrangling skills through practical examples and hands-on coding exercises in every lesson. By the end of the course, you'll have experience building end-to-end data engineering scripts. Whether you're a beginner looking to learn Python and SQL, or want to develop robust data engineering skills, this course will get you started. Enroll now and start collecting, storing, and working with data using Python and SQL You'll gain hands-on experience building Python scripts and SQL queries for common data engineering tasks. This course is divided in 4 weeks: Week 1 Working with Data in Python By the end of Week 1 you'll be able to: Apply Python data structures like lists, dicts Extract data from sources like CSV, JSON Load and persist data using JSON Lesson 1: Data Structures in Python Lesson Outline Lists, tuples, dictionaries Working with pandas DataFrames Loading data files like CSV into data structures Lesson 2: Reading and Writing Data Lesson Outline Reading and writing CSV files Serializing Python objects with JSON Parsing and dumping JSON data Lesson 3: Persisting and Loading Data in Python Lesson Outline Loading data from files Saving data from Python to disk Loading and saving data to JSON Week 2 Python Scripting and SQL By the end of Week 2 you'll be able to: Write reusable Python scripts Use SQLite to persist data Query SQLite databases with Python Lesson 1: Python Scripting Techniques Lesson Outline Writing modular, reusable Python scripts Exception handling and logging Python virtual environments Lesson 2: Python with SQLite Lesson Outline Creating SQLite databases from Python Writing tables with SQLAlchemy Querying SQLite from Python with SQLAlchemy Week 3 Learning Objectives By the end of Week 3 you'll be able to: Scrape and collect data from websites Build scalable scraping scripts Persist scraped data to files/databases Lesson 1: Web Scraping with Python Lesson Outline HTML parsing and structure Using Beautiful Soup for scraping Storing scraped data in Python Lesson 2: Scalable Web Scraping Lesson Outline Scraping best practices Scaling scraping with multiprocessing Storing scraped data in databases Week 4 Learning Objectives By the end of Week 4 you'll be able to: Connect to MySQL from Python Execute SQL statements and queries Import and export data from MySQL Lesson 1: Python and MySQL Lesson Outline Installing MySQL and configuration Connecting Python to MySQL Executing queries and statements Lesson 2: Running SQL queries from VSCode Use Visual Studio Code to build SQL queries Execute and review SQL queries from Visual Studio Code Lesson 3: Importing and Exporting Data Lesson Outline Loading and exporting CSV data Best practices for moving data into MySQL Automating data imports with Python About your instructor Alfredo Deza has over a decade of experience as a Software Engineer doing DevOps, automation, and scalable system architecture. Before getting into technology he participated in the 2004 Olympic Games and was the first-ever World Champion in High Jump representing Peru. He currently works in Developer Relations at Microsoft and is an Adjunct Professor at Duke University. This solid background in technology and teaching, including his experience teaching and authoring content about DevOps and MLOps will give you everything you need to get started applying these powerful concepts. Resources Python and Rust CLI Tools Linux For Beginners Hands on Python for MLOps.

Abstract: Learning Generative AI with AWS CodeWhisperer During Live Coding This video series covers live coding in various languages iteratively, thus learning the language. Lessons Covered Include 1.0 Getting started with Bash and Rust using AWS CodeWhisperer, VSCode, and AWS Toolkit for VSCode Learning Objectives Learn the fundamentals of Generative AI Learn to build AI-powered solutions with Bash, Rust, and AWS services Master the key syntax and features of Bash and Rust languages Additional Popular Resources Assimilate OpenAI 52 Weeks of AWS-The Complete Series Microsoft Azure Fundamentals (AZ-900) Certification Rust Bootcamp Python Bootcamp Google Professional Machine Learning Engineer Course 2023 (Rough Draft) Rust Data Engineering Building with the GitHub EcoSystem: Copilot, CodeSpaces, and GitHub Actions Microsoft Azure Fundamentals (AZ-900) Certification Google Professional Cloud Architect Certification Course 2023 (Rough Draft) AWS Solutions Architect Professional (SAP-C02) 2023.

Abstract: Using Rust with Python A Practical Guide to Rust for Python Developers Topics: Lesson 1: Introduction to Rust and Python Integration & PyO3 Basics Video 1.1 : Introduction to Rust and Python Video 1.2 : PyO3 Installation Video 1.3 : Basic Rust Library Video 1.4 : Rust to Python Video 1.5 : Rust Ownership Model Video 1.6 : Diagram PyO3 Project Video 1.7 : Python Calc CLI Video 1.8 : PyO3 Features Video 1.9 : PyO3 Exceptions Lesson 2: Advanced Rust and Python Mixing Techniques Video 1.10 : Call Python from Rust Video 1.11 : Run Python Embedded in Rust Video 1.12 : Embedded Rust CLI Diagram Video 1.13 : Embedded Rust CLI Video 1.14 : Embedded Rust CLI Test Video 1.15 : Rust-built Python Tools Video 1.16 : Using Rust Ruff Linter Lesson 3: Python and Rust Command-Line Tools & Recap Video 1.17 : Using Polars with Python and Rust Video 1.18 : Polars CLI in Rust Video 1.19 : Polars CLI Test in Rust Video 1.20 : Polars-CLI Integration Test Video 1.21 : Polars Criterion Benchmarking Learning Objectives Understand the need for Rust and Python integration. Explore the PyO3 library and its usage. Understand the Rust ownership model and its usage in PyO3. Who Should Take This Course Python developers who want to learn Rust and its integration with Python. Rust developers who want to learn how to integrate Rust with Python. Developers who want to learn how to build command-line tools using Rust and Python.

Abstract: Rust AWS Lambda Build and deploy serverless applications with Rust and AWS Lambda This course will help you build and deploy serverless applications with Rust and AWS Lambda. You will learn how to create, deploy, and manage AWS Lambda functions using Rust, leveraging the power and performance of the language for serverless architecture, and apply it by building a real-world Lambda function as a part of a distributed application. What you will learn Understand the concept of serverless architecture and AWS Lambda. Create and deploy AWS Lambda functions written in Rust. Integrate Lambda functions with other AWS services. Monitor, test, and optimize Rust-based Lambda functions. Implement real-world applications using Rust and AWS Lambda. Lesson 1: Getting Started with Rust AWS Lambda and Serverless Introduction to Serverless and AWS Lambda Walk through Rust Firecracker Project Setting up VSCode AWS Toolkit and CodeWhisperer for Rust Introduction to Cargo Lambda Rust Cost Advantage AWS Lambda Using the AWS Lambda Console Using the Step Functions Console Invoking Step Functions from AWS CLI Lesson 2: Advanced Techniques with Rust and AWS Lambda Building a Rust AWS Lambda Add Function Building a Rust AWS Lambda Divide by Two Function Invoking AWS Step Function from CLI Building Chainable AWS Step Functions with Rust Serverless MLOPs with EFS mounted in AWS Lambda Using Rust AWS Lambda Function URLs Saving money with arm64 Rust AWS Lambdas Lesson 3: Building a Polars Rust AWS Lambda Solution Polars Rust AWS Lambda Build and Deploy Polars Rust AWS Lambda Using AWS Console with deployed Polars Rust AWS Lambda Analyzing Rust AWS Lambda code with CodeWhisperer and AWS Toolkit AWS Lambda Function URLs Build and Deploy Polars Rust AWS Lambda Function URLs.

Abstract: GitHub Foundations: Privacy, Security and Administration (Rough Draft) Updated (JIT) as it is built About This Video Course Building Secure and Efficient GitHub Workflows This video series covers live coding the GitHub Administration and Security domain iteratively, thus learning the best practices and nuances of managing and securing GitHub projects. Lessons Covered Include: 1.0 Course Introduction: Building Secure and Efficient GitHub Workflows 1.1 Introduction to Authentication and Security: Ensuring Your GitHub Account's Integrity 1.2 Securing Account with 2FA 1.3 Understanding Access Permissions 1.4 Enterprise Managed Users (EMUs) Explained 1.5 Introduction to GitHub Administration: Configuring and Managing Repositories and Organizations 1.6 Enabling and Disabling Features 1.7 Repository Permission Levels Explained 1.8 Setting Repository Visibility Options 1.9 Repository Privacy Settings and Options 2.0 Features and Options in the Security Tab 2.1 Understanding Repository Insights 2.2 Managing Collaborators in GitHub 2.3 Managing Organization Settings 2.4 Members, Teams, and Roles in a GitHub Organization 2.5 Next Steps: Continuing Your GitHub Mastery Journey Learning Objectives Learn GitHub's authentication and security measures Learning to build solutions with robust repository management Learning the key syntax and features of GitHub Administration and Security Additional Popular Resources Assimilate OpenAI 52 Weeks of AWS-The Complete Series Microsoft Azure Fundamentals (AZ-900) Certification AWS Solutions Architect Professional (SAP-C02) 2023 Assimilate Python From Zero.

Abstract: Achieving Scalability with Vector, Graph, and Key/Value Databases Description This week we explore vector and graph databases, powerful tools for managing and extracting insights from large, complex datasets. As data volumes continue to grow, scalability is crucial. We'll learn how vector and graph databases can efficiently store data while maintaining relationships, enabling more advanced analytics. Through real-world examples, you'll see how these databases unlock scalability for machine learning, fraud detection, social networks, and more. Learning Objectives Learn Vector, Graph, SQLand Key/Value Databases Learning to build solutions with Rust, Vector, Graph, and Key/Value Databases Learning advanced data engineering Additional Popular Resources 52 Weeks of AWS-The Complete Series Microsoft Azure Fundamentals (AZ-900) Certification Rust Bootcamp Python Bootcamp Google Professional Machine Learning Engineer Course 2023 (Rough Draft) Rust Data Engineering Building with the GitHub EcoSystem: Copilot, CodeSpaces, and GitHub Actions Microsoft Azure Fundamentals (AZ-900) Certification Google Professional Cloud Architect Certification Course 2023 (Rough Draft) AWS Solutions Architect Professional (SAP-C02) 2023.

Abstract: Databricks Certified Data Engineer Associate Course 1: Databricks Lakehouse Platform Description Learn foundational Databricks capabilities including compute, storage, notebooks, and jobs to build scalable data solutions. Learning Objectives Create clusters and configure runtime environments Perform exploratory analysis with notebooks Schedule and monitor multi-task workflows Course 2: Databricks SQL Description Master Spark SQL for reading, transforming, and loading data at scale. Learn techniques like data validation, custom business logic, and slowly changing dimensions. Learning Objectives Query data in notebooks with Spark SQL Handle complex data types Apply data quality rules Implement slowly changing dimensions Course 3: Databricks ML Description Build ML models with Python and Scala APIs in Databricks. Learn best practices for feature engineering, hyperparameter tuning, and model evaluation. Learning Objectives Engineer features from raw data Tune models with cross validation Evaluate model performance Operationalize models with MLflow Course 4: Databricks Data Engineering Description Architect reliable and performant data infrastructure with Delta Lake, streaming, and autoscaling. Learning Objectives Implement ACID transactions Build streaming ETL solutions Autoscale infrastructure to meet SLAs Migrate data warehouses to lakehouse Course 5: Workloads with Jobs Description Orchestrate workloads using multi-task Jobs with configurable scheduling, dependencies, and error handling. Learning Objectives Schedule notebooks, jobs and pipelines Set dependencies across tasks Handle and retry failures Monitor runs using the Jobs UI Course 6: Data Access with Unity Catalog Description Provide governed data access across storage like ADLS, S3, and GCS using Unity Catalog. Learning Objectives Deploy a Unity Catalog Manage credentials securely Apply object-level security Query data from storage tiers Additional Popular Resources Assimilate OpenAI 52 Weeks of AWS-The Complete Series Microsoft Azure Fundamentals (AZ-900) Certification Rust Bootcamp Python Bootcamp Google Professional Machine Learning Engineer Course 2023 (Rough Draft) Rust Data Engineering Building with the GitHub EcoSystem: Copilot, CodeSpaces, and GitHub Actions Microsoft Azure Fundamentals (AZ-900) Certification Google Professional Cloud Architect Certification Course 2023 (Rough Draft) AWS Solutions Architect Professional (SAP-C02) 2023.

Abstract: Learn to build AWS Step Functions with Python and AWS Lambda. Use AWS Cloud9 to test your code locally and the Step Functions console to deploy your code to AWS. 00:00 Intro 00:22 Create AWS Lambda 02:49 Build Fruit Generator 05:01 Create New Test Event 07:18 Invoke AWS Lambda in Cloud9 09:32 Create AWS Step Functions 10:51 Create Second AWS Lambda 11:57 Create test payloads for second AWS Lambda 13:00 Build out the second AWS Lambda logic 15:11 Test bad logic condition 15:30 Build out chain reaction of AWS Step Functions.

Abstract: Learn to deploy containerized Microservices using Cloud9, ECR, App Runner and FastAPI automatically with AWS Code 00:00 0:37 Continuous Delivery Architecture 01:06 AWS App Runner 01:41 Building Containerized Microservices with FastAPI 03:22 Pinning requirements.txt to a specific version 06:10 Running FastAPI 10:40 Setup ECR 14:19 Setup ECR with AWS App Runner 16:10 Create containerized Flask API Microservice in Github Codespaces 18:54 Testing Swagger UI in Github Codespaces 20:11 Verify FastAPI Microservice in AWS App Runner 22:20 Setup AWS Code Build for Continuous Deployment with App Runner 30:12 Architectural Overview of Continuous Deployment 32:35 Verify Continuous Deployment with AWS Code Build and App Runner.

Abstract: Learn to build a Python Fire Command Line Interface (CLI) 00:00 Intro 00:27 Build Function to randomly generate fruit 01:45 Test out the function 02:31 Read Python Fire Documentation 03:00 Install Python Fire 03:40 Build Python Fire CLI 05:00 Invoke Python Fire Help Menu 05:22 Refactor Python Fire CLI 06:06 Test out the CLI with fruits.

Abstract: Learn how to use Python dictionaries to store and retrieve data. Although dictionaries look complex with different ways to represent, retrieve, and store data, they can be straightforward to use. In this course I'll show you how to create, store, retrieve, and loop over dictionaries. Once you go through these different actions, you should be able to choose the right approach to work effectively with dictionaries. If you are already familiar with Python lists and you are ready to take the next step with dictionaries, then this course is for you.

Abstract: Conda commands for beginners Learn how to use basic conda commands for developing Python If you are new to Conda, you might forget some of its basic commands. Even though I use it all the time, I forget how to remove an environment. In this video lesson, I'll cover the basics of environment creation, package installation, and environment removal. If you are only looking to find how to remove a Conda environment, no need to watch the whole lesson: conda env remove --name [ENV NAME] is the way to go! Conda is very popular because it allows you to install packages beyond Python packages like pip does. You can specify the Python version as well which I also cover in this lesson. Learning Objectives In this lesson you will learn: How to create a conda environment List available environments and check which one is activated Activate and deactivate a conda environment Remove conda environments along with Python packages Useful Resources Python for Beginners Learn Path Minimal Python book Free Azure Certification for Students.

Abstract: Learn to use AWS to continuously deploy a Hugo site that has global scale. 00:00 Intro 02:00 Create Github Repo 05:52 Launch AWS Cloud9 05:58 Download Hugo binary 11:00 Create Hugo Quickstart 17:00 Adding submodules for theme 23:00 Building Hugo locally and running it in Cloud9 26:00 Setup AWS S3 Static Hosting 30:30 Setup AWS CloudBuild for Continuous Delivery of Hugo 40:00 Verify Hugo deploy works.

Abstract: VSCode development environments Learn different options for programming with VSCode Find out different ways you can use VSCode for development: on the web, with Codespaces or locally with configurable Docker containers This video will cover what makes each one different and when you might want to use one vs. the other. From quick edits on GitHub to a more powerful machine backed by a remote compute using Codespaces, it is all about flexibility! Topics Covered Include: Use quick edits with GitHub.dev that show on your repository Open any repository with VSCode.dev Use Codespaces with a powerful remote compute Configure a local codespace with Devcontainers using Docker Additional Resources Pytest Master Class AWS Solutions Architect Professional Course Github Actions and GitOps in One Hour Video Course Jenkins CI/CD and Github in One Hour Video Course AWS Certified Cloud Practitioner Video Course Advanced Testing with Pytest Video Course AWS Solutions Architect Certification In ONE HOUR Python for DevOps Master Class 2022: CI/CD, Github Actions, Containers, and Microservices MLOPs Foundations: Chapter 2 Walkthrough of Practical MLOps Learn Docker containers in One Hour Video Course Introduction to MLOps Walkthrough AZ-900 (Azure Fundamentals) Quick reference guide.

Abstract: Python Functions and Classes Over 1 hour of course material with practice code Quickly learn learn the basics of Python functions and classes In this course, you will learn how to work with functions, classes and methods. These are the basis of almost any program you might create with Python. Functions and classes are useful for organizing code, increasing maintainability and code reuse. This course is perfect for beginners to Python that want to go beyond variables and logic and want to start working with object-oriented programming. This course includes example repositories with interactive Jupyter Notebooks that you can use to practice yourself. All lessons and videos have accompanying GitHub Repositories with example code. Reference GitHub Repository Learn Objectives This course includes the following learning objectives: In this week you will learn to: Create and work with functions. Build classes and methods. Require arguments or make them optional Use keyword arguments and use fallback values Resources Python Bootcamp for Data course Python for Beginners course Pytest Master Class Practical MLOps book.

Abstract: 52 Weeks of Rust Learning Rust by Live Coding This video series live codes Rust and iteratively learns the language. Lessons Covered Include: 1.0 52 weeks rust ep 1 getting started setup env 2.0 52 weeks rust ep 2 starting scripts 3.0 52 weeks rust ep 3 functions 4.0 52 weeks rust ep 4 loops 5.0 52 weeks rust ep 5 ownership more looping Learning Objectives Learn Rust by Live Coding Additional Popular Resources Pytest Master Class AWS Solutions Architect Professional Course Github Actions and GitOps in One Hour Video Course Jenkins CI/CD and Github in One Hour Video Course AWS Certified Cloud Practitioner Video Course Advanced Testing with Pytest Video Course AWS Solutions Architect Certification In ONE HOUR Python for DevOps Master Class 2022: CI/CD, Github Actions, Containers, and Microservices MLOPs Foundations: Chapter 2 Walkthrough of Practical MLOps Learn Docker containers in One Hour Video Course Introduction to MLOps Walkthrough AZ-900 (Azure Fundamentals) Quick reference guide 52 Weeks of AWS Episode 8: Infrastructure as Code with CDK and AWS Lambda Learn GCP Cloud Functions in One Hour Video Course Python Devops in TWO HOURS! MLOps Platforms From Zero: DatMLOps Platforms From Zero: Databricks, MLFlow/MLRun/SKLearn AWS Machine Learning Certification In ONE HOUR Fast, documented Machine Learning APIs with FastAPI Zero to One: AWS Lambda with SAM and Python in One Hour AWS Storage Solutions 2022: EBS/S3/EFS/Glacier Python Bootcamp for Data Testing In Python book Minimal Python book Practical MLOps book Python for DevOps-Playlist.

Abstract: Iteratively Build NLP Tools and FastAPI Microservice on AWS From Zero From Zero to NLP Hero This video will show you how to use CUDA GPUs with Python. Lessons Covered Include: 1.0 Introduction to Course 1.1 Introduction to Python Statements with Colab, IPython and GitHub CodeSpaces 2.0 Introduction to Python functions from simple to complex with Closure, and Decorators 3.0 Build a simple NLP Tool with TextBlob, Wikipedia and Python Fire command-line tool 4.0 Build an end-to-end NLP Microservice with FastAPI, ECR and AWS App Runner. Learning Objectives Learn key software engineering principles and best practices for building software. Learn to build NLP tools using TextBlob and Wikipedia. Learn to build a FastAPI containerized Microservice deployed via Amazon ECR and AWS App Runner. Additional Popular Resources Pytest Master Class AWS Solutions Architect Professional Course Github Actions and GitOps in One Hour Video Course Jenkins CI/CD and Github in One Hour Video Course AWS Certified Cloud Practitioner Video Course Advanced Testing with Pytest Video Course AWS Solutions Architect Certification In ONE HOUR Python for DevOps Master Class 2022: CI/CD, Github Actions, Containers, and Microservices MLOPs Foundations: Chapter 2 Walkthrough of Practical MLOps Learn Docker containers in One Hour Video Course Introduction to MLOps Walkthrough AZ-900 (Azure Fundamentals) Quick reference guide 52 Weeks of AWS Episode 8: Infrastructure as Code with CDK and AWS Lambda Learn GCP Cloud Functions in One Hour Video Course Python Devops in TWO HOURS! MLOps Platforms From Zero: DatMLOps Platforms From Zero: Databricks, MLFlow/MLRun/SKLearn AWS Machine Learning Certification In ONE HOUR Fast, documented Machine Learning APIs with FastAPI Zero to One: AWS Lambda with SAM and Python in One Hour AWS Storage Solutions 2022: EBS/S3/EFS/Glacier Python Bootcamp for Data Testing In Python book Minimal Python book Practical MLOps book Python for DevOps-Playlist.

Abstract: 52 Weeks of Swift Learning Swift by Live Coding This video series live coding Apple's Swift language and iteratively learning the language. Lessons Covered Include: 1.0 52 Weeks of Swift Episode 1 getting setup 2.0 52 Weeks of Swift Episode 2 the basics 3.0 52 Weeks of Swift Episode 3 basic operators 4.0 52 Weeks of Swift Episode 4 strings characters 5.0 52 Weeks of Swift Episode 5 collection types 6.0 52 Weeks of Swift Episode 6 Functions 7.0 52 Weeks of Swift Episode 7 Closures 8.0 52 Weeks of Swift Episode 8 Enumerations 9.0 52 Weeks of Swift Structures and Classes 10.0 52 Weeks of Swift Episode 10 Properties 11.0 52 Weeks of Swift Episode 11 Methods 12.0 52 Weeks of Swift Episode 12 Subscripts 13.0 52 Weeks of Swift Episode 13 Inheritance 14.0 52 Weeks of Swift Episode 14 Deinitialization 15.0 52 Weeks of Swift Episode 14 Initialization 16.0 52 Weeks of Swift_ Episode 16 Error Handling 17.0 52 Weeks of Swift_ Episode 17 Concurrency 18.0 52 Weeks of Swift_ Episode 18 Type Casting 19.0 52 Weeks of Swift_ Episode 19 Nested Types 20.0 52 Weeks of Swift Generics 21.0 52 weeks of Swift EP21 Protocols 22.0 52 weeks of Swift OpaqueTypes Learning Objectives Learn Apple's Swift language by Live Coding Additional Popular Resources Pytest Master Class AWS Solutions Architect Professional Course Github Actions and GitOps in One Hour Video Course Jenkins CI/CD and Github in One Hour Video Course AWS Certified Cloud Practitioner Video Course Advanced Testing with Pytest Video Course AWS Solutions Architect Certification In ONE HOUR Python for DevOps Master Class 2022: CI/CD, Github Actions, Containers, and Microservices MLOPs Foundations: Chapter 2 Walkthrough of Practical MLOps Learn Docker containers in One Hour Video Course Introduction to MLOps Walkthrough AZ-900 (Azure Fundamentals) Quick reference guide 52 Weeks of AWS Episode 8: Infrastructure as Code with CDK and AWS Lambda Learn GCP Cloud Functions in One Hour Video Course Python Devops in TWO HOURS! MLOps Platforms From Zero: DatMLOps Platforms From Zero: Databricks, MLFlow/MLRun/SKLearn AWS Machine Learning Certification In ONE HOUR Fast, documented Machine Learning APIs with FastAPI Zero to One: AWS Lambda with SAM and Python in One Hour AWS Storage Solutions 2022: EBS/S3/EFS/Glacier Python Bootcamp for Data Testing In Python book Minimal Python book Practical MLOps book Python for DevOps-Playlist.

Abstract: Applied Hugging Face Learn to use Hugging Face to solve real-world problems 1.0 Introduction to Applied Hugging Face Course 1.1 Introduction to Hugging Face Hub: models, datasets, spaces and access tokens 1.2 Using GPU enabled Codespaces 1.3 Using Hugging Face CLI 1.4 Working with Datasets 1.5 Working with Models 2.0 Introduction to Transformers and Fine-tuning vs Supervized Learning (Transfer Learning) 2.1 Using Hugging Face with GitHub GPU-enabled Codespaces for Fine-tuning 3.0 Introduction to Hugging Face Spaces 3.1 Using Hugging Face Spaces 3.2 Deploying Hugging Face Spaces applications with GitHub Actions Topics Covered Include: Hugging Face Hugging Face Models Hugging Face Transformers Hugging Face Spaces Hugging Face Datasets GPU-enabled GitHub Codespaces OpenAI Whisper Hugging Face Tutorial Additional Popular Resources Pytest Master Class AWS Solutions Architect Professional Course Github Actions and GitOps in One Hour Video Course Jenkins CI/CD and Github in One Hour Video Course AWS Certified Cloud Practitioner Video Course Advanced Testing with Pytest Video Course AWS Solutions Architect Certification In ONE HOUR Python for DevOps Master Class 2022: CI/CD, Github Actions, Containers, and Microservices MLOPs Foundations: Chapter 2 Walkthrough of Practical MLOps Learn Docker containers in One Hour Video Course Introduction to MLOps Walkthrough AZ-900 (Azure Fundamentals) Quick reference guide 52 Weeks of AWS Episode 8: Infrastructure as Code with CDK and AWS Lambda Learn GCP Cloud Functions in One Hour Video Course Python Devops in TWO HOURS! MLOps Platforms From Zero: DatMLOps Platforms From Zero: Databricks, MLFlow/MLRun/SKLearn AWS Machine Learning Certification In ONE HOUR Fast, documented Machine Learning APIs with FastAPI Zero to One: AWS Lambda with SAM and Python in One Hour AWS Storage Solutions 2022: EBS/S3/EFS/Glacier Python Bootcamp for Data Testing In Python book Minimal Python book Practical MLOps book Python for DevOps-Playlist.

Abstract: Assimilate F Learning Fsharp During Live Coding This video series covers live coding the Fsharp Language iteratively thus learning the language. Lessons Covered Include: 1.0 Setup Fsharp in Visual Studio Code and GitHub Codespaces and build interactive Fsharp scripts and projects Learning Objectives Learn Fsharp Learning to build solutions with Fsharp Learning the key syntax and features of the FsharpLanguage Additional Popular Resources Pytest Master Class AWS Solutions Architect Professional Course Github Actions and GitOps in One Hour Video Course Jenkins CI/CD and Github in One Hour Video Course AWS Certified Cloud Practitioner Video Course Advanced Testing with Pytest Video Course AWS Solutions Architect Certification In ONE HOUR Python for DevOps Master Class 2022: CI/CD, Github Actions, Containers, and Microservices MLOPs Foundations: Chapter 2 Walkthrough of Practical MLOps Learn Docker containers in One Hour Video Course Introduction to MLOps Walkthrough AZ-900 (Azure Fundamentals) Quick reference guide 52 Weeks of AWS Episode 8: Infrastructure as Code with CDK and AWS Lambda Learn GCP Cloud Functions in One Hour Video Course Python Devops in TWO HOURS! MLOps Platforms From Zero: DatMLOps Platforms From Zero: Databricks, MLFlow/MLRun/SKLearn AWS Machine Learning Certification In ONE HOUR Fast, documented Machine Learning APIs with FastAPI Zero to One: AWS Lambda with SAM and Python in One Hour AWS Storage Solutions 2022: EBS/S3/EFS/Glacier Python Bootcamp for Data Testing In Python book Minimal Python book Practical MLOps book Python for DevOps-Playlist.

Abstract: Assimilate SQL Learning SQL During Live Coding This video series covers live coding the SQL Language iteratively thus learning the language. Lessons Covered Include: 1.0 Creating a GitHub Codespace with Python and PostgreSQL integration and finishes with a successful SQL query Learning Objectives Learn SQL Learning to build solutions with SQL and other languages like Python, Javascript and more. Learning the key syntax and features of the SQL Language Additional Popular Resources Pytest Master Class AWS Solutions Architect Professional Course Github Actions and GitOps in One Hour Video Course Jenkins CI/CD and Github in One Hour Video Course AWS Certified Cloud Practitioner Video Course Advanced Testing with Pytest Video Course AWS Solutions Architect Certification In ONE HOUR Python for DevOps Master Class 2022: CI/CD, Github Actions, Containers, and Microservices MLOPs Foundations: Chapter 2 Walkthrough of Practical MLOps Learn Docker containers in One Hour Video Course Introduction to MLOps Walkthrough AZ-900 (Azure Fundamentals) Quick reference guide 52 Weeks of AWS Episode 8: Infrastructure as Code with CDK and AWS Lambda Learn GCP Cloud Functions in One Hour Video Course Python Devops in TWO HOURS! MLOps Platforms From Zero: DatMLOps Platforms From Zero: Databricks, MLFlow/MLRun/SKLearn AWS Machine Learning Certification In ONE HOUR Fast, documented Machine Learning APIs with FastAPI Zero to One: AWS Lambda with SAM and Python in One Hour AWS Storage Solutions 2022: EBS/S3/EFS/Glacier Python Bootcamp for Data Testing In Python book Minimal Python book Practical MLOps book Python for DevOps-Playlist.

Abstract: AWS Certified Machine Learning - Specialty (MLS-C01)-2023 Learn to pass the AWS Certified Machine Learning - Specialty (MLS-C01) exam This video series prepares you to pass the AWS Certified Machine Learning - Specialty (MLS-C01) exam. The AWS Certified Machine Learning - Specialty (MLS-C01) exam is a pass or fail exam. The exam is scored against a minimum standard established by AWS professionals who follow certification industry best practices and guidelines. Domain 1: Data Engineering 1.0 course intro 1.1 technology prerequisite 1.2 sagemaker studio lab 1.3 learn aws cloudshell 1.4 cloud developer workspace advantage 1.5 prototyping ai apis aws cloudshell bash 1.6 cloud9 with codewhisperer 1.7 domain one intro 1.8 data storage 1.9 determine storage medium 1.10 using s3 demo 1.11 job styles batch vs streaming 1.12 data ingestion pipelines 1.13 aws batch demo 1.14 step function demo Domain 2: Exploratory Data Analysis 2.0 domain intro Sanitize and prepare data for modeling 2.1 cleanup data 2.2 scaling data 2.3 labeling data 2.4 mechanical turk labeling Perform feature engineering 2.5 identify extract features 2.6 feature engineering concepts Analyze and visualize data for machine learning 2.7 graphing data 2.9 clustering Conclusion 2.10 conclusion Domain 3: Modeling 3.0 domain intro Frame business problems as machine learning problems 3.1 when to use ml 3.2 supervised vs unsupervized 3.3 selection right ml solution Select the appropriate model(s) for a given machine learning problem 3.4 select models 3.5 sagemaker canvas demo Train machine learning models 3.6 train test split 3.7 optimization 3.8 compute choice Perform hyperparameter optimization 3.14 neural network architecture Evaluate machine learning models 3.18 overfitting vs underfitting 3.19 selecting metrics 3.22 compare models experiment tracking Conclusion 3.23 Conclusion Domain 4: Machine Learning Implementation and Operations 4.0 course intro Build machine learning solutions for performance, availability, scalability, resiliency, and fault 4.1 logging monitoring 4.2 multiple regions 4.3 reproducible workflow 4.4 aws flavored devops Recommend and implement the appropriate machine learning services and features for a given 4.5 provisioning ec2 4.5 compute choices 4.6 provisioning ebs 4.7 aws ai ml services Apply basic AWS security practices to machine learning solutions. 4.9 plp aws lambda 4.10 integrated security Deploy and operationalize machine learning solutions 4.13 sagemaker workflow 4.14 doing predictions with sagemaker canvas 4.16 retrain models Conclusion 5.0 course conclusion Topics Covered Include: Domain 1: Data Engineering Domain 2: Exploratory Data Analysis Domain 3: Modeling Domain 4: Machine Learning Implementation and Operations Additional Popular Resources Pytest Master Class AWS Solutions Architect Professional Course Github Actions and GitOps in One Hour Video Course Jenkins CI/CD and Github in One Hour Video Course AWS Certified Cloud Practitioner Video Course Advanced Testing with Pytest Video Course AWS Solutions Architect Certification In ONE HOUR Python for DevOps Master Class 2022: CI/CD, Github Actions, Containers, and Microservices MLOPs Foundations: Chapter 2 Walkthrough of Practical MLOps Learn Docker containers in One Hour Video Course Introduction to MLOps Walkthrough AZ 900 (Azure Fundamentals) Quick reference guide 52 Weeks of AWS Episode 8: Infrastructure as Code with CDK and AWS Lambda Learn GCP Cloud Functions in One Hour Video Course Python Devops in TWO HOURS! MLOps Platforms From Zero: DatMLOps Platforms From Zero: Databricks, MLFlow/MLRun/SKLearn AWS Machine Learning Certification In ONE HOUR Fast, documented Machine Learning APIs with FastAPI Zero to One: AWS Lambda with SAM and Python in One Hour AWS Storage Solutions 2022: EBS/S3/EFS/Glacier Python Bootcamp for Data Testing In Python book Minimal Python book Practical MLOps book Python for DevOps Playlist.

Abstract: Microsoft Azure Data Scientist Associate (DP-100)-2023 Learn DP-100 and get certified as an Azure Data Scientist This video series covers the Azure Data Scientist Associate (DP-100). Lessons Covered Include: Domain 1: Design and prepare a machine learning solution 1.0 course intro 1.1 prerequisite technology 1.2 compute specification workload ml training 1.3 create azure ml workspace 1.4 explore azure ml workspace 1.5 create manage datasets 1.6 create compute 1.7 monitor compute Domain 2: Explore data and train models 2.1 load transform data 2.2 analyze data azure data explorer 2.3 azure data explorer demo 2.5 use designer 2.7 automl azure ml studio 2.9 develop with jupyter notebook 2.10 develop with visual studio code 2.11 train model python sdk Domain 3: Prepare a model for deployment 3.1 configure compute for job run 3.2 GitHub to Azure Cloud Shell Feedback Loop 3.3 explore open datasets python sdk 3.4 explore azure ml cli 3.9 describe MLflow model output azure ml studio databricks 3.10 mlops mlflow tracking 3.11 dbmlops open source mlflow Domain 4: Deploy and retrain a model 4.1 real time and batch deployment 4.3 end to end ml databricks mlflow 4.4 dbmlops end to end mlops on databricks 4.5 using azure open datasets automl 4.6 trigger Azure Machine Learning Pipeline GitHub 5.0 conclusion Learning Objectives Manage Azure Resources for Machine Learning Run Experiments and Train Models Deploy and Operationalize Machine Learning Solutions Implement Responsible Machine Learning Additional Popular Resources Pytest Master Class AWS Solutions Architect Professional Course Github Actions and GitOps in One Hour Video Course Jenkins CI/CD and Github in One Hour Video Course AWS Certified Cloud Practitioner Video Course Advanced Testing with Pytest Video Course AWS Solutions Architect Certification In ONE HOUR Python for DevOps Master Class 2022: CI/CD, Github Actions, Containers, and Microservices MLOPs Foundations: Chapter 2 Walkthrough of Practical MLOps Learn Docker containers in One Hour Video Course Introduction to MLOps Walkthrough AZ-900 (Azure Fundamentals) Quick reference guide 52 Weeks of AWS Episode 8: Infrastructure as Code with CDK and AWS Lambda Learn GCP Cloud Functions in One Hour Video Course Python Devops in TWO HOURS! MLOps Platforms From Zero: DatMLOps Platforms From Zero: Databricks, MLFlow/MLRun/SKLearn AWS Machine Learning Certification In ONE HOUR Fast, documented Machine Learning APIs with FastAPI Zero to One: AWS Lambda with SAM and Python in One Hour AWS Storage Solutions 2022: EBS/S3/EFS/Glacier Python Bootcamp for Data Testing In Python book Minimal Python book Practical MLOps book Python for DevOps-Playlist.

Abstract: Assimilate Azure Cloud Learning Azure During Live Coding This video series covers live coding the Azure Cloud. Lessons Covered Include: 1.0 getting started Azure cloud shell and console Learning Objectives Learn Azure Cloud Learning to build solutions with Azure Learning the key syntax and features of Azure Additional Popular Resources Pytest Master Class AWS Solutions Architect Professional Course Github Actions and GitOps in One Hour Video Course Jenkins CI/CD and Github in One Hour Video Course AWS Certified Cloud Practitioner Video Course Advanced Testing with Pytest Video Course AWS Solutions Architect Certification In ONE HOUR Python for DevOps Master Class 2022: CI/CD, Github Actions, Containers, and Microservices MLOPs Foundations: Chapter 2 Walkthrough of Practical MLOps Learn Docker containers in One Hour Video Course Introduction to MLOps Walkthrough AZ-900 (Azure Fundamentals) Quick reference guide 52 Weeks of AWS Episode 8: Infrastructure as Code with CDK and AWS Lambda Learn GCP Cloud Functions in One Hour Video Course Python Devops in TWO HOURS! MLOps Platforms From Zero: DatMLOps Platforms From Zero: Databricks, MLFlow/MLRun/SKLearn AWS Machine Learning Certification In ONE HOUR Fast, documented Machine Learning APIs with FastAPI Zero to One: AWS Lambda with SAM and Python in One Hour AWS Storage Solutions 2022: EBS/S3/EFS/Glacier Python Bootcamp for Data Testing In Python book Minimal Python book Practical MLOps book Python for DevOps-Playlist.

Abstract: Assimilate C++ Learning C++ During Live Coding This video series covers live coding the Language iteratively thus learning the language. Lessons Covered Include: 1.0 github codespaces cpp hello world calculator Learning Objectives Learn C++ by live coding Learning to build solutions with C++ Learning the key syntax and features of the C++ Language Additional Popular Resources Pytest Master Class AWS Solutions Architect Professional Course Github Actions and GitOps in One Hour Video Course Jenkins CI/CD and Github in One Hour Video Course AWS Certified Cloud Practitioner Video Course Advanced Testing with Pytest Video Course AWS Solutions Architect Certification In ONE HOUR Python for DevOps Master Class 2022: CI/CD, Github Actions, Containers, and Microservices MLOPs Foundations: Chapter 2 Walkthrough of Practical MLOps Learn Docker containers in One Hour Video Course Introduction to MLOps Walkthrough AZ-900 (Azure Fundamentals) Quick reference guide 52 Weeks of AWS Episode 8: Infrastructure as Code with CDK and AWS Lambda Learn GCP Cloud Functions in One Hour Video Course Python Devops in TWO HOURS! MLOps Platforms From Zero: DatMLOps Platforms From Zero: Databricks, MLFlow/MLRun/SKLearn AWS Machine Learning Certification In ONE HOUR Fast, documented Machine Learning APIs with FastAPI Zero to One: AWS Lambda with SAM and Python in One Hour AWS Storage Solutions 2022: EBS/S3/EFS/Glacier Python Bootcamp for Data Testing In Python book Minimal Python book Practical MLOps book Python for DevOps-Playlist.

Abstract: AWS Certified Advanced Networking - Specialty (ANS-C01)-2023 Learning AWS Certified Advanced Networking This video series the AWS Certified Advanced Networking Certification 2023 Lessons Covered Include: 1.0 course intro 1.1 sap prerequisite 1.2 Domain intro 1.3 CDN Pattern 1.4 ELB Design 1.5 Amazon API Gateway 1.6 Route 53 Demo 1.7 Public vs Private Hosted Zones DNS-DEMO 1.8 Load Balancing Layer 3, 4, 7 of OSI Model 1.9 Cloudwatch Metrics Demo 1.1 Layer 1/2 concepts: VLAN, Jumbo Frames 1.11 Multiple AWS Accounts 1.12 VPC Peering 2.0 domain intro 2.1 Infrastructure Automation (IAC) with CDK Demo 2.2 Hybrid Networks 2.3 Authentication and Authorization 2.4 Network Monitoring 2.5 Traffic Management DNS 2.6 AWS Boto3 Cloud9 CodeWhisper Demo 2.7 AWS CLI Demo 3.0 domain intro 3.1 Quotas and Limits in AWS Networking 3.2 Inter-Regional communication patterns 3.3 Network performance testing tools- demo 3.4 VPC Flow logs - Demo 3.5 Different types of Network interfaces AWS 3.6 High-Availability via Route 53 4.0 domain intro 4.1 common security threats 4.2 network acls 4.3 AWS Cloud Trail - Demo 4.4 AWS Trusted Advisor Demo 4.5 Network encryption options AWS 4.6 AWS Shared Security Model 5.0 conclusion Learning Objectives Network Design (25%) Network Implementation (25%) Network Management and Operations (25%) Network Security, Compliance, and Governance Additional Popular Resources Pytest Master Class AWS Solutions Architect Professional Course Github Actions and GitOps in One Hour Video Course Jenkins CI/CD and Github in One Hour Video Course AWS Certified Cloud Practitioner Video Course Advanced Testing with Pytest Video Course AWS Solutions Architect Certification In ONE HOUR Python for DevOps Master Class 2022: CI/CD, Github Actions, Containers, and Microservices MLOPs Foundations: Chapter 2 Walkthrough of Practical MLOps Learn Docker containers in One Hour Video Course Introduction to MLOps Walkthrough AZ-900 (Azure Fundamentals) Quick reference guide 52 Weeks of AWS Episode 8: Infrastructure as Code with CDK and AWS Lambda Learn GCP Cloud Functions in One Hour Video Course Python Devops in TWO HOURS! MLOps Platforms From Zero: DatMLOps Platforms From Zero: Databricks, MLFlow/MLRun/SKLearn AWS Machine Learning Certification In ONE HOUR Fast, documented Machine Learning APIs with FastAPI Zero to One: AWS Lambda with SAM and Python in One Hour AWS Storage Solutions 2022: EBS/S3/EFS/Glacier Python Bootcamp for Data Testing In Python book Minimal Python book Practical MLOps book Python for DevOps-Playlist.

Abstract: Python Functions Master Class 2023: Master Functions in Python and Deploy to AWS, GCP, and Azure Learning Python Fuctions During Live Coding This video series covers live coding the Language iteratively thus learning the language. Lessons Covered Include: 1.0 Master Functions in Python and Deploy to AWS, GCP, and Azure while learning click, fire and FastAPI Learning Objectives Learn Python Functions Learning to build solutions for the cloud using Python Learning the key syntax and features of the Python Language Additional Popular Resources Pytest Master Class AWS Solutions Architect Professional Course Github Actions and GitOps in One Hour Video Course Jenkins CI/CD and Github in One Hour Video Course AWS Certified Cloud Practitioner Video Course Advanced Testing with Pytest Video Course AWS Solutions Architect Certification In ONE HOUR Python for DevOps Master Class 2022: CI/CD, Github Actions, Containers, and Microservices MLOPs Foundations: Chapter 2 Walkthrough of Practical MLOps Learn Docker containers in One Hour Video Course Introduction to MLOps Walkthrough AZ-900 (Azure Fundamentals) Quick reference guide 52 Weeks of AWS Episode 8: Infrastructure as Code with CDK and AWS Lambda Learn GCP Cloud Functions in One Hour Video Course Python Devops in TWO HOURS! MLOps Platforms From Zero: DatMLOps Platforms From Zero: Databricks, MLFlow/MLRun/SKLearn AWS Machine Learning Certification In ONE HOUR Fast, documented Machine Learning APIs with FastAPI Zero to One: AWS Lambda with SAM and Python in One Hour AWS Storage Solutions 2022: EBS/S3/EFS/Glacier Python Bootcamp for Data Testing In Python book Minimal Python book Practical MLOps book Python for DevOps-Playlist.

Abstract: Azure Databricks with Pandas and Open Datasets. Find out how to get a working cluster with Databricks using Azure and then use the full Pandas API operating in the cluster with Open Datasets and a Python Jupyter Notebook. This video will walk you through creating a workspace in Azure to create the Databricks service, then create the cluster that comes with the Pyspark Pandas API, and finally import the open datasets into the cluster. Although straightforward to create a Databricks cluster with Azure, it is a bit more involved to run a Python Jupyter Notebook that has Azure ML Open Datasets installed and availabe in the cluster along with the ability to use the full Pandas API you are used to working with and taking advantage of the clustering capabilities from Databricks.

Abstract: What is the if __name__ condition in Python for? If you've seen __main__ at the end of a Python script and wondered why that is there it is to avoid side effects when importing and allowing you to run the file as a script on the terminal. If you want more details, watch this short video where we work with a Python script to find out those side effects and how the Python script can change once the if condition with __name__ is done at the end. Next Steps Now that you know about the conditional at the end, you might want to explore writing Python scripts that use this trick. There are several examples you can follow in these GitHub repositories with argparse, the Click framework and using sys.argv: argparse GitHub repo examples sys.argv GitHub repo examples Build Python applications from scratch.

Abstract: Learn to build greedy algorithms, then convert them to microservices and finally containerized them and deploy to AWS with App Runner. 00:00 Intro 00:38 Github Code Repo of Traveling Salesman Problem 02:00 Overview of Cities in Problem 04:50 Launching Github Codespaces to work on problem 06:30 Cleaning up the code and fixing Makefile and Lint 09:30 Running simulations and minimizing the cost 14:00 Pinning version numbers in requirements.txt 16:00 Setup Github Actions 19:00 Fixing Python Black error 21:32 Create Github Actions Status Badge 25:00 Creating FastAPI Microservice 28:00 Building Greedy Coin Change Microservice 36:00 Deploying Python FastAPI Microservice to AWS with App Runner 46:00 Theory of logic to containerization 51:00 Building Containerized C# Service in AWS Cloud9 and App Runner 01:03:44 Testing via Curl in AWS CloudShell 01:06:00 Building Containerized FastAPI Service in AWS Cloud9 and App Runner 01:11:03 Pushing to Amazon ECR 01:14:00 Testing via FastAPI App Runner API with Swagger UI.

Abstract: Learn to use AWS Storage Solutions Correctly. Hi, my name is Noah gift, and today I'm going to talk about AWS storage systems. We're going to talk about the elastic box system where EBS, also S3, the object storage system, which has unlimited data storage, and also EFS, the elastic file system. And finally, offsite backup, and also archiving systems with glacier, we're also going to do some demos of those products. Let's go ahead and get started. This video shoes both theory and hands on demos. * EBS (Elastic Block Storage) * EFS (Elastic File System) * S3 (Object Storage) * Glacier (Archiving) 00:00 Intro 00:49 Storage Module Overview 03:00 What is EBS? 11:00 EBS Demos 30:00 What is S3? 38:00 S3 Demos 48:00 Boto3 S3 Demos 49:00 What is EFS? 51:47 What is AWS Glacier?.

Abstract: Swift Classes and Structures 00:00 Intro.

Abstract: What does the Yield keyword do in Python? This video covers the basics so you can understand what yield is, what is a generator, and why is it different than any other iterable in Python A yield keyword makes a function a lazy iterable. Producing values one at a time. The function becomes a generator, so when called, the code will not execute. A generator is a function that uses the yield keyword. This function is an iterable that produces values one at a time, or does a lazy production of values. I'll use a Jupyter Notebook that you can use to follow along with practical examples that will show you what are some of the key differences and when or why you would use the yield keyword. In this video you will learn: What is an iterable and how is it different from using yield? What does yield do to a function? What is a Python generator Potential uses for a Python generator like infinite results Useful Resources GitHub repository with examples Build Python applications from scratch Try Azure for Free Introduction to Azure Databricks.

Abstract: Learn to use MLOPs methodology to deploy a Hugging Face Spaces application using Github Actions. 00:00 Intro 00:35 Project Overview of Hugging Face Spaces, Gradio and Github Actions 01:23 Hugging Face Overview 01:52 Creating Hugging Face Spaces 02:28 Creating Github Repo and Github Codespaces with 16 Cores 03:37 Setup Gradio Application and Python Structure 06:12 Installing Tensorflow and running Gradio application with Github Codespaces browser 06:48 Using Hugging Face Token 07:26 Setup Github Actions Secrets with Token from Hugging Face 08:00 Setup Github Actions that pushes to Hugging Face Spaces 09:30 Testing Deployed Hugging Face Spaces Application.

Abstract: Learn to provision EC2 instances from both the AWS Console and AWS CloudShell. Also learn the key components of EC2 instance provisioning. 00:00 Intro 00:45 EC2 Instance Provisioning Key Component Diagram Walkthrough 03:18 Launching an EC2 Instance from AWS Console 04:06 Creating SSH key pair 04:46 Configuring Security Group rules 05:21 Walkthrough of Advanced Setting including User Data 06:33 Observing EC2 Instance State in Console 07:07 Using AWS CloudShell to launch and controll EC2 Instances 07:44 Finding AMI IDs to launch EC2 Instances from AMI Catalog 08:00 Launching EC2 Instances from AMI Catalog with AWS CloudShell 08:52 Connecting to EC2 Instances from AWS Console and logging into Linux shell 09:40 Walkthrough of EC2 Instance Connection options for Windows with RDP 10:56 Terminating Instances with AWS CloudShell and Bash AWS CLI 11:50 Recap of EC2 Instance Provisioning.

Abstract: Learn to go from theory to DevOps to MLOps platforms in this MLOps Master Class. 00:00 Intro 01:18 Noah Gift Background 04:14 Why do we need MLOPs? 05:06 Where the data science industry is headed? 06:57 Without DevOps you don't have MLOps 08:46 Continuous delivery is enabled by the Cloud and IAC 10:03 DataOps is like the water hookup in your home 11:23 Platform Automation solves the complexity of the data science industry 15:06 MLOPs Feedback loop 16:33 Create Once, but Deploy Everywhere. Good Example is Google AutoML 18:16 MLOps isn't data centric or model centric there is no silver bullet 21:52 MLOps use cases: Autonomous Driving is a good example 23:00 How to invest in technology: Primary and Secondary and Research 25:50 AWS and Azure are the leaders in the cloud 27:39 Secondary considerations: Splunk, Snowflake, BigQuery, Iguazio, etc 29:00 Leverage learning platform and metacognition 30:00 Key certifications 32:00 NFSOps is using managed file systems to build new cloud-native workflows 34:00 Kubernetes is the new gold standard for many distributed systems 35:00 Sagemaker has many use cases 36:21 Azure ML Studio 37:21 Google Vertex AI 37:48 Iguazio MLRun 41:00 Current issues in distributed systems 45:00 Apple Create ML Demo 51:00 Databricks Spark Clusters 57:00 MLFlow 01:00:37 What is DevOps? 01:03:16 Creating a new Github repo 01:05 Developering with AWS Cloud9 01:20:26 Setup Github Actions 01:23:00 Walkthrough of Python MLOps cookbook example using a sklearn project 01:35:00 Pushing sklearn flask microservice to Amazon ECR 01:39:00 Setup AWS App Runner for MLOps Microservice inference 01:43:00 Setup Continuous Delivery of MLOps Microservice using AWS Code Build \5880 Online resource; title from title details screen (O’Reilly, viewed June 2, 2022).02:06:00 Comparing MLOps Platforms Databricks, Sagemaker and MLRun 02:31:00 Deploying MLRun open source MLOps with Colab Notebook.

Abstract: VSCode for the web Use VSCode directly on the web for editing Git repos Although you can use VSCode locally, using it in the web allows you to avoid having an install and dealing with platform problems. There is no account required and you can directly edit GitHub repositories on the web. In the case of Python, there are a couple of constraints that you must be aware of, like no support to run the terminal and execute a Python program. But syntax and intellisense works great, and we can even make Jupyter Notebooks work with the help of an extension. If you are a beginner and trying out different text editors, or even if you are a seasoned developer, you will find a case to use this feature of VSCode. Learning Objectives In this lesson you will learn: Opening a GitHub repository directly on the web and start editing it Commit and push changes from VSCode on the web to GitHub What are the constraints and limitations of the Python support Use Jupyter Notebooks and find what are some of the problems you may find Useful Resources VSCode for the Web documentation Minimal Python book Free Azure Certification for Students Develop web applications with Visual Studio Code.

Abstract: Automated deletion of Azure Resources Prevent surprise bills with automation Prevent your Azure bill from running wild! In this video I show how you can automate the removal of Azure resources on demand or by schedule. With GitHub Actions and an Azure account you can prevent services from running wild and causing unnecessary billing overcharges! With automation in place, there is no need to remember what VM you left running! First, you'll start by creating a GitHub Action that authenticates to Azure, then you will query locally your resource groups to identify which ones you can delete. Next, you will port the query over to the Action workflow. Finally, you will delete resources that match, making sure those are fully removed, preventing a large bill. You will need a GitHub and Azure account, an Azure Service Principal and the Azure CLI installed locally. Learning Objectives In this lesson you will learn: Create a GitHub Action workflow to query Resource Groups Query Resource Groups that match locally Use scripting to extract Resource Group names Remove Resource Groups with the Azure CLI in GitHub Actions Useful Resources Sample GitHub repository Create an Azure Service Principal for GitHub Actions Azure Service Principal documentation Install Azure CLI Free Azure Certification for Students Introduction to GitHub Actions Run Python In GitHub Actions.

Abstract: Learn to use a CUDA GPU to speed up code in Python dramatically. CUDA POWERED! This video will show you how to use CUDA GPUs with Python. Topics Covered Include: 00:00 Introduction 00:16 End of Moore's Law 01:15 What is a TPU and ASIC 02:25 How a GPU works 03:05 Enabling GPU in Colab Notebook 04:16 Using Python Numba 05:40 Building Mandlebrots with and without GPU and Numba 07:49 CUDA Vectorize Functions 08:27 Copy Data to GPU Memory Learning Objectives Learn about the End of Moore's Law and ASICs Learn to compare the performance of GPUs and TPUs, and JIT compilers Build your own vectorized functions with Numba and CUDA to speed up your code with GPUs Additional Popular Resources Pytest Master Class AWS Solutions Architect Professional Course Github Actions and GitOps in One Hour Video Course Jenkins CI/CD and Github in One Hour Video Course AWS Certified Cloud Practitioner Video Course Advanced Testing with Pytest Video Course AWS Solutions Architect Certification In ONE HOUR Python for DevOps Master Class 2022: CI/CD, Github Actions, Containers, and Microservices MLOPs Foundations: Chapter 2 Walkthrough of Practical MLOps Learn Docker containers in One Hour Video Course Introduction to MLOps Walkthrough AZ-900 (Azure Fundamentals) Quick reference guide 52 Weeks of AWS Episode 8: Infrastructure as Code with CDK and AWS Lambda Learn GCP Cloud Functions in One Hour Video Course Python Devops in TWO HOURS! MLOps Platforms From Zero: DatMLOps Platforms From Zero: Databricks, MLFlow/MLRun/SKLearn AWS Machine Learning Certification In ONE HOUR Fast, documented Machine Learning APIs with FastAPI Zero to One: AWS Lambda with SAM and Python in One Hour AWS Storage Solutions 2022: EBS/S3/EFS/Glacier Python Bootcamp for Data Testing In Python book Minimal Python book Practical MLOps book Python for DevOps-Playlist.

Abstract: DevOps Theory to Practice Go from theory to implementation in this DevOps course with Python and AWS This video course will show you how to start your first day as a DevOps Engineer and progress. Topics Covered Include: DevOps Theory Learn useful Python skills for DevOps including working with the filesystem and processes. Build an AWS Boto3 tool with Python Fire for translation and test it with CI/CD. Build a containerized FastAPI service deployed to AWS App Runner through continuous delivery. Learning Objectives Learn the theory behind DevOps Walk through the implementation of several O'Reilly books including Python for DevOps, AWS for C# and Enterprise MLOps. Build an AWS Translation Service tool with Python and Boto3 Deploy a Pandas FastAPI web service with Python and FastAPI and AWS App Runner Build and use AWS CodeBuild with AWS App Runner Additional Popular Resources Pytest Master Class AWS Solutions Architect Professional Course Github Actions and GitOps in One Hour Video Course Jenkins CI/CD and Github in One Hour Video Course AWS Certified Cloud Practitioner Video Course Advanced Testing with Pytest Video Course AWS Solutions Architect Certification In ONE HOUR Python for DevOps Master Class 2022: CI/CD, Github Actions, Containers, and Microservices MLOPs Foundations: Chapter 2 Walkthrough of Practical MLOps Learn Docker containers in One Hour Video Course Introduction to MLOps Walkthrough AZ-900 (Azure Fundamentals) Quick reference guide 52 Weeks of AWS Episode 8: Infrastructure as Code with CDK and AWS Lambda Learn GCP Cloud Functions in One Hour Video Course Python Devops in TWO HOURS! MLOps Platforms From Zero: DatMLOps Platforms From Zero: Databricks, MLFlow/MLRun/SKLearn AWS Machine Learning Certification In ONE HOUR Fast, documented Machine Learning APIs with FastAPI Zero to One: AWS Lambda with SAM and Python in One Hour AWS Storage Solutions 2022: EBS/S3/EFS/Glacier Python Bootcamp for Data Testing In Python book Minimal Python book Practical MLOps book Python for DevOps-Playlist.

Abstract: Python Testing for Beginners Over 1 hour of course material with practice code Quickly learn learn how to test in Python Learn the basics of Python testing in this course. From a brief overview of the standard library to using a more modern approach with Pytest, one of the most popular testing libraries in Python. By the end of this week, you should be comfortable working with existing tests, creating new tests, and debugging test failures. All lessons and videos have accompanying GitHub Repositories with example code. Reference GitHub Repository Learning Objectives In this course you will learn to: Understand the basics of testing and its importance Write and run tests with functions and classes Use Pytest for running and creating tests Apply different techniques for debugging including PDB and stopping on failure Use fixtures to simplify tests that make identical assertions Resources Python for beginners course Python Functions and Classes course Python Dictionaries course Pytest Master Class Python Bootcamp for Data Practical MLOps book.

Abstract: MLOps Key Concepts - An Ongoing Series Select MLOps Key Concepts This video series live coding languages iteratively thus learning the language. Lessons Covered Include: 1.0 MLOps Hierarchy of Needs: DevOps, DataOps, Platform Automation and MLOps Learning Objectives Learn key terminology and concepts in machine learning, data science, data operations and DevOps Understand foundational concepts in MLOps Additional Popular Resources Pytest Master Class AWS Solutions Architect Professional Course Github Actions and GitOps in One Hour Video Course Jenkins CI/CD and Github in One Hour Video Course AWS Certified Cloud Practitioner Video Course Advanced Testing with Pytest Video Course AWS Solutions Architect Certification In ONE HOUR Python for DevOps Master Class 2022: CI/CD, Github Actions, Containers, and Microservices MLOPs Foundations: Chapter 2 Walkthrough of Practical MLOps Learn Docker containers in One Hour Video Course Introduction to MLOps Walkthrough AZ-900 (Azure Fundamentals) Quick reference guide 52 Weeks of AWS Episode 8: Infrastructure as Code with CDK and AWS Lambda Learn GCP Cloud Functions in One Hour Video Course Python Devops in TWO HOURS! MLOps Platforms From Zero: DatMLOps Platforms From Zero: Databricks, MLFlow/MLRun/SKLearn AWS Machine Learning Certification In ONE HOUR Fast, documented Machine Learning APIs with FastAPI Zero to One: AWS Lambda with SAM and Python in One Hour AWS Storage Solutions 2022: EBS/S3/EFS/Glacier Python Bootcamp for Data Testing In Python book Minimal Python book Practical MLOps book Python for DevOps-Playlist.

Abstract: Heuristics, Metaheuristics, and Algorithms in Python Get deeper into the matrix by diving into advanced computer science weekly updated This video course will show iteratively how to build advanced algorithms. 1.0 Advanced Heuristics and Algorithms in Python greedy coin traveling salesman geopy Learning Objectives Create a heuristic algorithm using TSP, a traveling salesman problem Dive into greedy algorithms Learn about metaheuristics Additional Popular Resources Pytest Master Class AWS Solutions Architect Professional Course Github Actions and GitOps in One Hour Video Course Jenkins CI/CD and Github in One Hour Video Course AWS Certified Cloud Practitioner Video Course Advanced Testing with Pytest Video Course AWS Solutions Architect Certification In ONE HOUR Python for DevOps Master Class 2022: CI/CD, Github Actions, Containers, and Microservices MLOPs Foundations: Chapter 2 Walkthrough of Practical MLOps Learn Docker containers in One Hour Video Course Introduction to MLOps Walkthrough AZ-900 (Azure Fundamentals) Quick reference guide 52 Weeks of AWS Episode 8: Infrastructure as Code with CDK and AWS Lambda Learn GCP Cloud Functions in One Hour Video Course Python Devops in TWO HOURS! MLOps Platforms From Zero: DatMLOps Platforms From Zero: Databricks, MLFlow/MLRun/SKLearn AWS Machine Learning Certification In ONE HOUR Fast, documented Machine Learning APIs with FastAPI Zero to One: AWS Lambda with SAM and Python in One Hour AWS Storage Solutions 2022: EBS/S3/EFS/Glacier Python Bootcamp for Data Testing In Python book Minimal Python book Practical MLOps book Python for DevOps-Playlist.

Abstract: Doing Hugging Face Building Solutions with Hugging Face This video course will show you how to build solutions with Hugging Face. Topics Covered Include: 1.0 Introduction to Course 1.1 Learn to use MLOPs methodology to deploy a Hugging Face Spaces application using Github Actions. Learning Objectives Build applications with Hugging Face Apply MLOPs methodology to deploy a Hugging Face Spaces application using Github Actions. Additional Popular Resources Pytest Master Class AWS Solutions Architect Professional Course Github Actions and GitOps in One Hour Video Course Jenkins CI/CD and Github in One Hour Video Course AWS Certified Cloud Practitioner Video Course Advanced Testing with Pytest Video Course AWS Solutions Architect Certification In ONE HOUR Python for DevOps Master Class 2022: CI/CD, Github Actions, Containers, and Microservices MLOPs Foundations: Chapter 2 Walkthrough of Practical MLOps Learn Docker containers in One Hour Video Course Introduction to MLOps Walkthrough AZ-900 (Azure Fundamentals) Quick reference guide 52 Weeks of AWS Episode 8: Infrastructure as Code with CDK and AWS Lambda Learn GCP Cloud Functions in One Hour Video Course Python Devops in TWO HOURS! MLOps Platforms From Zero: DatMLOps Platforms From Zero: Databricks, MLFlow/MLRun/SKLearn * AWS Machine Learning Certification In ONE HOUR Fast, documented Machine Learning APIs with FastAPI Zero to One: AWS Lambda with SAM and Python in One Hour AWS Storage Solutions 2022: EBS/S3/EFS/Glacier Python Bootcamp for Data Testing In Python book Minimal Python book WT.mc_id=academic-0000-alfredodeza) Practical MLOps book Python for DevOps-Playlist.

Abstract: Introduction to Azure Functions Create a function with Python and Flask Learn how to create an Azure Function in the fastest way using instant deployment with Visual Studio Code, and then find out how to take it to the next level by using the Flask framework to take advantage of common Python patterns you might already be used to. Finally, we'll go through automation steps using GitHub Actions so that you can re-deploy your Azure Function after merging changes. This will give you confidence for deployment, make it less error-prone, and give you a foundational workflow to implement serverless technology using the Azure cloud. Learning Objectives Create an Azure Function using the Azure Portal Quickly build a function using Visual Studio Code Deploy in one click with the Azure Functions extension Modify your code and routes to use Flask Use GitHub Actions for automating the deployment process About your instructor Alfredo Deza has over a decade of experience as a Software Engineer doing DevOps, automation, and scalable system architecture. Before getting into technology he participated in the 2004 Olympic Games and was the first-ever World Champion in High Jump representing Peru. He currently works in Developer Relations at Microsoft and is an Adjunct Professor at Duke University. This solid background in technology and teaching, including his experience creating courses with Codespaces is seen throughout this course, where you will get a first-hand experience with practical examples as well as applicable configuration for any development environment. Resources GitHub Codespaces course Microsoft Azure Fundamentals (AZ-900) Certification Pytest Master Class Practical MLOps book.

Abstract: AWS Solutions Architect Professional (SAP-C02) 2023 Learn from AWS Certification Author, Five Time O'Reilly Author and AWS ML Hero Noah Gift Getting certified by AWS can be a big boost for your career in cloud application development and hybrid architectural design. In this course, a four-part certification prep series--instructor Noah Gift, an AWS ML Hero, shows you the skills you need to know to prepare for and tackle the AWS Certified Solutions Architect Professional exam (SAP-C02). Discover the benefits of the AWS Certified Solutions Architect Professional certification, which validates your ability to design, deploy, and evaluate applications on AWS within diverse, complex requirements. Learn about the two question formats you'll encounter on the exam, the exam content outline, the scoring rubric, and the recommended prerequisites for aspiring candidates. Along the way, Noah reviews cross-account authentication and access strategy, designing networks, and setting up multi-account AWS environments. In Domain 1: Design Solutions for Organizational Complexity, you learn: What you need to know about AWS networking concepts, hybrid DNS concepts, and network segmentation. What you need to be skilled in troubleshooting traffic flows and utilizing service endpoints. Finally, you learn to develop a strategy for centralized security event In Domain 2: Design for New Solutions, you learn to design a deployment strategy to: meet business requirements ensure business continuity determine security controls, design a system to meet reliability requirements optimize costs In Domain 3: Continuous Improvement for Existing Solutions, you learn to determine a strategy to improve overall operational excellence. This process may include alerting and automatic remediation strategies, disaster recovery planning, monitoring and logging solutions, and CI/CD pipelines and deployment strategies. Finally, you determine a system to improve security and how to handle data retention. In Domain 4: Accelerate Workload Migration and Modernization, you learn to be familiar with tools and services for workload migration and modernization, including: assessment and tracking tools portfolio assessment asset planning prioritization and migration of workloads data migration options and tools application migration tools AWS networking services and DNS identity services databases Lessons Include: Learning Objectives This course covers the following learning objectives: Studying for the AWS Certified Solutions Architect Professional exam (SAP-C02) Mastering advanced AWS with Hands on screencasts of popular AWS services Understanding the AWS Certified Solutions Architect Professional exam (SAP-C02) Additional Popular Resources Pytest Master Class AWS Solutions Architect Professional Course Github Actions and GitOps in One Hour Video Course Jenkins CI/CD and Github in One Hour Video Course AWS Certified Cloud Practitioner Video Course Advanced Testing with Pytest Video Course AWS Solutions Architect Certification In ONE HOUR Python for DevOps Master Class 2022: CI/CD, Github Actions, Containers, and Microservices MLOPs Foundations: Chapter 2 Walkthrough of Practical MLOps Learn Docker containers in One Hour Video Course Introduction to MLOps Walkthrough AZ-900 (Azure Fundamentals) Quick reference guide 52 Weeks of AWS Episode 8: Infrastructure as Code with CDK and AWS Lambda Learn GCP Cloud Functions in One Hour Video Course Python Devops in TWO HOURS! MLOps Platforms From Zero: DatMLOps Platforms From Zero: Databricks, MLFlow/MLRun/SKLearn AWS Machine Learning Certification In ONE HOUR Fast, documented Machine Learning APIs with FastAPI Zero to One: AWS Lambda with SAM and Python in One Hour AWS Storage Solutions 2022: EBS/S3/EFS/Glacier Python Bootcamp for Data Testing In Python book Minimal Python book Practical MLOps book Python for DevOps-Playlist.

Abstract: Assimilate HTML/CSS/JS Learning Assimilate HTML/CSS/JS During Live Coding This video series covers live coding the Assimilate HTML/CSS/JS iteratively thus learning the language. Lessons Covered Include: 1.0 Get started with HTML, CSS and render out several static websites using node.js express. Learning Objectives Learn HTML/CSS/JS Learning to build solutions with HTML/CSS/JS Learning the key syntax and features of the HTML/CSS/JS Additional Popular Resources Pytest Master Class AWS Solutions Architect Professional Course Github Actions and GitOps in One Hour Video Course Jenkins CI/CD and Github in One Hour Video Course AWS Certified Cloud Practitioner Video Course Advanced Testing with Pytest Video Course AWS Solutions Architect Certification In ONE HOUR Python for DevOps Master Class 2022: CI/CD, Github Actions, Containers, and Microservices MLOPs Foundations: Chapter 2 Walkthrough of Practical MLOps Learn Docker containers in One Hour Video Course Introduction to MLOps Walkthrough AZ-900 (Azure Fundamentals) Quick reference guide 52 Weeks of AWS Episode 8: Infrastructure as Code with CDK and AWS Lambda Learn GCP Cloud Functions in One Hour Video Course Python Devops in TWO HOURS! MLOps Platforms From Zero: DatMLOps Platforms From Zero: Databricks, MLFlow/MLRun/SKLearn AWS Machine Learning Certification In ONE HOUR Fast, documented Machine Learning APIs with FastAPI Zero to One: AWS Lambda with SAM and Python in One Hour AWS Storage Solutions 2022: EBS/S3/EFS/Glacier Python Bootcamp for Data Testing In Python book Minimal Python book Practical MLOps book Python for DevOps-Playlist.

Abstract: Assimilate Bash Learning Bash During Live Coding This video series covers live coding the Bash Language iteratively thus learning the language. Lessons Covered Include: 1.0 Setup Bash GitHub Codespace for Bash and then build a phrase repeator cli with help menu and error handling Learning Objectives Learn Bash Learning to build solutions with Bash the shell Learning the key syntax and features of the Bash Language Additional Popular Resources Pytest Master Class AWS Solutions Architect Professional Course Github Actions and GitOps in One Hour Video Course Jenkins CI/CD and Github in One Hour Video Course AWS Certified Cloud Practitioner Video Course Advanced Testing with Pytest Video Course AWS Solutions Architect Certification In ONE HOUR Python for DevOps Master Class 2022: CI/CD, Github Actions, Containers, and Microservices MLOPs Foundations: Chapter 2 Walkthrough of Practical MLOps Learn Docker containers in One Hour Video Course Introduction to MLOps Walkthrough AZ-900 (Azure Fundamentals) Quick reference guide 52 Weeks of AWS Episode 8: Infrastructure as Code with CDK and AWS Lambda Learn GCP Cloud Functions in One Hour Video Course Python Devops in TWO HOURS! MLOps Platforms From Zero: DatMLOps Platforms From Zero: Databricks, MLFlow/MLRun/SKLearn AWS Machine Learning Certification In ONE HOUR Fast, documented Machine Learning APIs with FastAPI Zero to One: AWS Lambda with SAM and Python in One Hour AWS Storage Solutions 2022: EBS/S3/EFS/Glacier Python Bootcamp for Data Testing In Python book Minimal Python book Practical MLOps book Python for DevOps-Playlist.

Abstract: AWS Certified Data Analytics (DAS-C01) - Specialty-2023 Learning to take the AWS Certified Data Analytics Certification Exam This video series how to study for the AWS Certified Data Analytics Certification Exam. Lessons Covered Include: 1.0 Introduction to the AWS Certified Data Analytics (DAS-C01) - Specialty-2023 1.1 technology prerequisite 1.2 sagemaker studio lab 1.3 learn aws cloudshell 1.4 cloud developer workspace advantage 1.5 prototyping ai apis aws cloudshell bash 1.6 cloud9 with codewhisperer Learning Objectives Domain 1: Collection Determine the operational characteristics of the collection system Select a collection system that handles the frequency, volume, and source of data Select a collection system that addresses the key properties of data, such as order, format, and compression 1.0 Introduction to the AWS Certified Data Analytics (DAS-C01) - Specialty-2023 1.1 technology prerequisite 1.2 sagemaker studio lab 1.3 learn aws cloudshell 1.4 cloud developer workspace advantage 1.5 prototyping ai apis aws cloudshell bash 1.6 cloud9 with codewhisperer 1.7 operationalizing collection system 1.11 job styles batch vs streaming 1.12 data ingestion pipelines 1.13 aws batch demo 1.14 step function demo 1.15 transform data in transit 1.16 handle ml specific map reduce 1.17 install rust cloud9 1.18 build aws rust s3 size calculator Domain 2: Storage and Data Management Determine the operational characteristics of the storage solution for analytics Determine data access and retrieval patterns Select the appropriate data layout, schema, structure, and format Define data lifecycle based on usage patterns and business requirements Determine the appropriate system for cataloging data and managing metadata 2.0 domain intro 2.2 one size database 2.3 serverless data engineering 2.4 provisioning ebs 2.5 retrain models 2.6 use s3 storage 2.7 big data challenges 2.8 build systems tools with rust deduplication Domain 3: Processing Determine appropriate data processing solution requirements Design a solution for transforming and preparing data for analysis Automate and operationalize data processing solutions 3.0 domain intro 3.1 kubernetes key concepts 3.2 kubernetes clust 3.3 kubernetes pods nodes 3.4 services deployments 3.5 running minikube 3.6 minikube fastapi kubernetes 3.7 building tiny container 3.8 aws api app runner 3.9 pytorch fastapi deploy app runner 3.10 aws app runner csharp 3.11 options container orchestration 3.12 AWS ECS Fargate Dotnet Microservice 3.1* 3 load testing locust 3.14 sre mindset mlops 3.15 compute choices 3.16 provisioning ec2 Domain 4: Analysis and Visualization Determine the operational characteristics of the analysis and visualization solution Select the appropriate data analysis solution for a given scenario Select the appropriate data visualization solution for a given scenario 4.0 domain intro 4.1 cleanup data 4.2 scaling data 4.3 labeling data 4.4 mechanical turk labeling 4.5 identify extract features 4.6 feature engineering concepts 4.7 graphing data 4.9 clustering 4.11 kaggle nba quicksight 4.12 sagemaker canvas demo 4.13 train test split Domain 5: Security Select appropriate authentication and authorization mechanisms Apply data protection and encryption techniques Apply data governance and compliance controls 5.0 intro domain 5.1 security access strategy 5.2 firewall 5.5 aws trusted advisor demo 5.6 shared security model 5.7 encryption in transit rest 5.8 data protection aws 5.9 audit network 5.10 integrated security 5.11 plp lambda 5.12 analyze logs aws 5.13-conclusion Additional Popular Resources Pytest Master Class AWS Solutions Architect Professional Course Github Actions and GitOps in One Hour Video Course Jenkins CI/CD and Github in One Hour Video Course AWS Certified Cloud Practitioner Video Course Advanced Testing with Pytest Video Course AWS Solutions Architect Certification In ONE HOUR Python for DevOps Master Class 2022: CI/CD, Github Actions, Containers, and Microservices MLOPs Foundations: Chapter 2 Walkthrough of Practical MLOps Learn Docker containers in One Hour Video Course Introduction to MLOps Walkthrough AZ-900 (Azure Fundamentals) Quick reference guide 52 Weeks of AWS Episode 8: Infrastructure as Code with CDK and AWS Lambda Learn GCP Cloud Functions in One Hour Video Course Python Devops in TWO HOURS! MLOps Platforms From Zero: DatMLOps Platforms From Zero: Databricks, MLFlow/MLRun/SKLearn AWS Machine Learning Certification In ONE HOUR Fast, documented Machine Learning APIs with FastAPI Zero to One: AWS Lambda with SAM and Python in One Hour AWS Storage Solutions 2022: EBS/S3/EFS/Glacier Python Bootcamp for Data Testing In Python book Minimal Python book Practical MLOps book Python for DevOps-Playlist.

Abstract: Learn to use pure Cloud-Native tools to developer AWS Microservices in .NET 6.0. These services include: Cloud9, ECR, App Runner and Cloudshell. 00:00 Intro 00:43 AWS Microservices Architecture 02:00 Github C# Project Structure 02:23 AWS Cloud9 03:00 Building Docker Images for .NET 6 Web Service 04:00 Push to ECR 05:00 Setup AWS App Runner 06:47 Test service with AWS Cloudshell.

Abstract: Learn to use K-Means clustering from theory to implementation 00:00 Intro 00:47 Theory of Machine Learning 05:00 Colab Notebook exploration of K-Means Algorithm 06:00 Distance Metrics 08:00 Creating K-Means Pipeline 12:00 Elbow Plots 14:00 Silhouette Plots 17:00 Running K-Means Serial Simulation 18:34 Running K-Means Parallel Simulation 20:00 Spinning up Huge Cloud9 128 GB Ram 32 vCPU Instance 23:00 Running massively parallel K-Means.

Abstract: Learn to refactor a Python script into a library called by Python Click CLI. 00:00 Intro 00:19 Refactoring to a library 01:44 Installing click 02:23 Importing refactored library 02:36 Building click options 05:04 Formatting code with Python Black and Running Click 06:14 Building conditional logic to print colored output.

Abstract: Execute SQL with CSV datasets SQL is usually reserved for interacting with databases but in this video I show how you can use Databricks to run SQL queries against a CSV dataset. There are a few defaults that can make working with a CSV dataset problematic, like disabled schema infering and no headers. These are crucial when running SQL against the CSV since the defaults will treat every single value as a string. Although this video uses Azure Databricks, the same concepts should apply to any Databricks cluster. In this video you will learn: Uploading a CSV dataset to Databricks Create a Notebook to work with the CSV dataset Find potential pitfalls in default options for CSV and SQL Useful Resources Azure Databricks, Pandas, and Opendatasets Free Azure Certification for Students Learn Azure Databricks fundamentals Try Azure for Free Introduction to Azure Databricks.

Abstract: Deploy a containerized web app to Azure There are many services out there that allow you to deploy a container to the cloud. Although a straightforward operation, it is critical to understand how to create a robust automation deployment system. This course will create a Python Flask application from scratch, will containerize it, set the automation for it and then deploy it to Azure. This course lays out the foundations for creating more powerful containerized applications beyond the simple examples. You can add a Machine Learning model into the container and the end result would be the same: a robust deployment system that can get changes quickly onto Azure with any changes to the main branch. The continuous deployment system is a game changer as a skill set, making you a far more valuable engineer. In this video you will learn: Create a Python Flask application for containerization Use a Dockerfile to create the Flask container Use GitHub Actions to automatically deploy the container after a pull request or changes to the main branch Push your container to the GitHub container registry from the GitHub repository Create the web app on the Azure portal and connect it to GitHub Actions Host the containerized Python Flask application on Azure Debugging techniques for broken containers and other common errors like incorrect ports Useful Resources GitHub repository with sample code Free Azure Certification for Students Try Azure for Free AZ-900 Azure Fundamentals reference guide Azure SAMBA file share Azure Remote Compute for VSCode.

Abstract: Learn to build cloud pipelines with AWS Step Functions 00:00 Intro 01:00 Create AWS Lambda function in Python 04:00 Create return statement that returns JSON depending on type of event 05:49 Test the AWS Lambda function 08:00 Build a second AWS Lambda function that can parse first function 13:00 Create AWS Step Function that consumes both AWS Lambda functions for Pipeline.

Abstract: Learn about Swift Properties.

Abstract: Learn to build real-world Python Microservices that enable Continuous Delivery. 00:00 Intro 05:00 Scaffolding a project in Python 08:00 Setup Virtualenv 13:10 Building Makefile 24:00 Setup Github Actions 29:00 Formatting code with Python Black 45:09 Test code with Pytest and Pytest Coverage 50:30 Using Python Fire to build CLI 59:30 Write Wikipedia scraper 1:02:00 Use IPython to interact and debug code in Github Codespaces 1:08:00 Pinning FastAPI version number 1:12:00 Building FastAPI Microservice 1:18:00 Using Text blob NLP service to parse phrases 1:29:00 Debugging broken code 1:34:00 Building container 1:54:00 Setup AWS Code Build push to ECR (Elastic Container Registry) 2:02:00 Setup AWS Code Build to ECR to AWS App Runner Continuous Delivery View the code on Github here: https://github.com/noahgift/python-for-devops-april-2022.

Abstract: Learn to setup Mac M1 with Tensorflow and Metal 00:00 Intro 01:17 Install Conda and tensorflow-metal 02:02 Run Juypter Notebook 02:10 Train a model using Tensorflow and M1 GPU 02:34 Verify GPU is saturated 03:25 Verify Activity Monitor shows GPU and CPU history for Python 3.9.

Abstract: Learn to script EC2 by using PowerShell inside of AWS CloudShell. I use AWSPowerShell.NetCore to query how many availability zones are available in my AWS region. 00:00 Intro 00:42 AWS Console 01:16 Invoking PowerShell inside of AWS CloudShell 01:33 Checking out AWS PowerShell Gallery 02:00 Importing via Import-Module AWSPowerShell.NetCore 03:00 Query All AWS Availability Zones in Region 03:30 Filter and Count by using a PowerShell Object with .count.

Abstract: Learn to deploy a AWS ECS Fargate application for a .NET Blazor App. 00:00 Intro 00:54 Overview of the project and the requirements 02:17 Setting up Cloud9 for .NET 6 03:36 Creating .NET Blazor 04:08 Run locally using dotnet run --urls=http://localhost:8080 05:45 Create Dockerfile 07:28 Run dotnet aws deploy to deploy to AWS ECS 09:12 Grab ECS endpoint and test the application and inspect ECS.

Abstract: MLOps packaging: HuggingFace and Docker Hub Use automation to package models Learn how to package a HuggingFace GPT2 model using automation with MLOps and pushing the result to Docker Hub. With just a little bit of Python and FastAPI you can have a powerful text generation API that is self-documented! Learn Objectives In this video lesson, I'll go over the details with an example repository you can use for reference including the following learning objectives: Create a FastAPI application with HuggingFace Interact with the model with HTTP from a container using FastAPI Containerize the application using GitHub Actions Create repository secrets to login and push to Docker Hub Resources Example repository Practical MLOps book MLOps Maturity Model Packaging ML models.

Abstract: MLOps deployment to Azure Container Apps Take advantage of insta-scaling for live inferencing Learn how to deploy an ML container with FastAPI and deploy it to Azure Container Apps with GitHub Actions. This repository gives you a good starting point with a Dockerfile, GitHub Actions workflow, and Python code that already works for generating text using GPT-2 with HuggingFace Transformers. First, you'll go through an architectural overview of the end-result, then you will go through every configuration item needed to set the automation right between Azure and GitHub Actions and the GitHub Container Registry. Finally, you'll see how to deploy and find a few crucial requirements needed for everything to work, like ingress ports and setting the right amount of RAM and CPU. Learn Objectives In this video lesson, I'll go over the details with an example repository you can use for reference including the following learning objectives: Use GitHub Container Registry to push a built container Use the Azure CLI in a GItHub Action to authenticate to Azure How to generate an Azure Service Principal and a Personal Access Token to authenticate Configure Azure Container Apps to correctly serve a model with enough resources Look at deployment logs to ensure things are working right Resources Example repository Practical MLOps book MLOps Maturity Model Packaging ML models MLOps packaging: HuggingFace and Docker MLOps packaging: HuggingFace and Azure Container Registry.

Abstract: GitHub Codespaces and custom dotfiles Add your dotfiles to any Codespace automatically Customize any GitHub Codespace you create or use by automatically getting your dotfiles in there with no effort! You only need a GitHub repository that contains your dotfiles to get this feature enabled in any Codespace you work on You also have the ability to have a setup.sh script that you can use to further customize how GitHub will add your dotfiles to your Codespace at creation time. Learning Objectives In this lesson you will learn: Configure a GitHub Codespace with dotfiles Create a repository with your own dotfiles Additional features you can try with dotfiles in Codespaces Useful Resources Dotfiles and Codespaces documentation Free Azure Certification for Students Introduction to GitHub Actions Run Python In GitHub Actions.

Abstract: Learn to use Github CodeSpaces and Github Actions to Build and Test C# and XUnit projects. 00:00 Intro 01:03 Create Github Project for C# 01:27 Create Github Codespace 16 core Visual Studio environment 03:15 Create Xunit project with dotnet cli 04:04 Create XUnit test 04:40 Find build and test commands for project 06:51 Setup and run GitHub Actions project 08:04 Verify Github Actions Compiles and Tests C#.

Abstract: Learn to use Hugo a popular Go-based content management system to deploy global-scale websites on AWS. 00:00 Intro 00:53 The need to spread information that helps humans at scale 02:26 Hugo Continuous Delivery with AWS CodeBuild workflow diagram 06:04 AWS CodeBuild 06:38 AWS Cloud9 07:56 Download Hugo and run it locally 09:40 Follow Hugo's Quickstart guide 11:36 Edit inbound rules for Cloud9 to preview running Hugo website 13:10 Looking at the buildspec.yml file for continuous delivery of Hugo website with AWS S3 and Cloudfront and Route 53 14:52 Walkthrough of AWS S3 static hosting 15:45 Walkthrough of AWS CodeBuild continuous delivery for Hugo console settings.

Abstract: Learn to master the Databricks platform for doing MLOps.

Abstract: Assimilate Python From Zero Learn Python From the Absolute Beginning This video series covers Python from the absolute beginning with incremental weekly lessons. Introduction Python Basics and How To Run Python Topics Covered Include: Python Basics Python Syntax Mastering Python Learn Python week-by-week Python Strings Additional Popular Resources Pytest Master Class AWS Solutions Architect Professional Course Github Actions and GitOps in One Hour Video Course Jenkins CI/CD and Github in One Hour Video Course AWS Certified Cloud Practitioner Video Course Advanced Testing with Pytest Video Course AWS Solutions Architect Certification In ONE HOUR Python for DevOps Master Class 2022: CI/CD, Github Actions, Containers, and Microservices MLOPs Foundations: Chapter 2 Walkthrough of Practical MLOps Learn Docker containers in One Hour Video Course Introduction to MLOps Walkthrough AZ-900 (Azure Fundamentals) Quick reference guide 52 Weeks of AWS Episode 8: Infrastructure as Code with CDK and AWS Lambda Learn GCP Cloud Functions in One Hour Video Course Python Devops in TWO HOURS! MLOps Platforms From Zero: DatMLOps Platforms From Zero: Databricks, MLFlow/MLRun/SKLearn AWS Machine Learning Certification In ONE HOUR Fast, documented Machine Learning APIs with FastAPI Zero to One: AWS Lambda with SAM and Python in One Hour AWS Storage Solutions 2022: EBS/S3/EFS/Glacier Python Bootcamp for Data Testing In Python book Minimal Python book Practical MLOps book Python for DevOps-Playlist.

Abstract: 52 Weeks of AWS-The Complete Series Master AWS with weekly episodes on all significant AWS certifications This video series covers all major AWS Certification exams, from the AWS Certified Developer Associate to the AWS Certified Solutions Architect. Lessons Include: 1.0-52-weeks-aws-introduction 2.0-52 Weeks AWS Episode 2-Getting Started with AWS 3.0-52 Weeks of AWS_ Episode 3_ AWS Cloud Practitioner Part 1 4.0-52 Weeks of AWS-Episode 4-Global-Infrastructure-AWS Cloud Practitioner Part 2 5.0-52 Weeks of AWS Episode 5_ Cloud Practitioner Part3 + Network and Content Delivery, Compute_Storag 6.0-52 Weeks of AWS Live Stream_ Episode 6-Final AWS CP Certification Walkthrough 7.0-52 Weeks of AWS-Episode 7_ Developing on AWS 8.0-Episode 8 52 weeks of AWS 9.0-52 weeks AWS episode 9 solutions architect-Beginning 10.0-52 Weeks AWS_ Episode 10-Compute 11.0-52 Weeks AWS_ Episode 11- Solutions Architect-Database 12.0-52 Weeks AWS_ Episode 12-Solutions Architect-Networking 13.0-52 Weeks AWS_ Episode 13-Solutions Architect-Connecting Networks 14.0-52 Weeks of AWS Episode 9_AWS Cookbook 16.0-Weeks AWS_ Episode 16-)Solutions Architect-Securing-Users 17.0-Weeks AWS_ Episode 17-Solutions Architect Continued-Elasticity 18.0-52 Weeks AWS_ Episode 18-Solutions Architect Continued-Automate Infra 19.0-52 Weeks AWS_ Episode 19-Solutions Architect Continued-Caching Content 20.0-52 Weeks AWS_ Episode 20-SA- Continued-Building Decoupled Architectures 21.0-52 Weeks AWS_ Episode 21-SA- Building Microservices 22.0-52 Weeks AWS_ Episode 22-SA-Planning for Disaster 23.0-52 Weeks AWS_ Episode 23-SA-Bridging to Certification 24.0-52 Weeks AWS_ Episode 24-AWS ML Certification-Welcome to ML foundations 25.0-52 Weeks AWS_ Episode 25-AWS ML Certification_ Wk2-Introduction to ML 26.0-52 Weeks AWS_ Episode 25-AWS ML Certification_ Wk3-Amazon SageMaker-Part2 27.0-52 Weeks AWS_ Episode 25-AWS ML Certification_ Wk3-Amazon SageMaker 28.0-52 Weeks AWS_ Episode 27-AWS ML Certification_ Continued 29.0-52 Weeks AWS_ Episode 28-AWS ML Certification Continued 30.0-52-weeks-aws-Episode-29-AWS-ML-Certification-Conclusions 29-AWS Certified Machine Learning Engineering-Conclusion Topics Covered Include: AWS MLOps AWS Certified Cloud Practitioner AWS Certified Solutions Architect AWS Certified Machine Learning AWS Certified Developer Additional Popular Resources Pytest Master Class AWS Solutions Architect Professional Course Github Actions and GitOps in One Hour Video Course Jenkins CI/CD and Github in One Hour Video Course AWS Certified Cloud Practitioner Video Course Advanced Testing with Pytest Video Course AWS Solutions Architect Certification In ONE HOUR Python for DevOps Master Class 2022: CI/CD, Github Actions, Containers, and Microservices MLOPs Foundations: Chapter 2 Walkthrough of Practical MLOps Learn Docker containers in One Hour Video Course Introduction to MLOps Walkthrough AZ-900 (Azure Fundamentals) Quick reference guide 52 Weeks of AWS Episode 8: Infrastructure as Code with CDK and AWS Lambda Learn GCP Cloud Functions in One Hour Video Course Python Devops in TWO HOURS! MLOps Platforms From Zero: DatMLOps Platforms From Zero: Databricks, MLFlow/MLRun/SKLearn AWS Machine Learning Certification In ONE HOUR Fast, documented Machine Learning APIs with FastAPI Zero to One: AWS Lambda with SAM and Python in One Hour AWS Storage Solutions 2022: EBS/S3/EFS/Glacier Python Bootcamp for Data Testing In Python book Minimal Python book Practical MLOps book Python for DevOps-Playlist.

Abstract: MLOps packaging: HuggingFace and GitHub Container Registry Use automation to package models to GitHub Learn how to package a HuggingFace GPT2 model using automation with MLOps and pushing the result to GitHub Container Registry. With just a little bit of Python and FastAPI you can have a powerful text generation API that is self-documented! Automation is a foundational piece of MLOps, and using GitHub Actions to package a model automatically and on-demand with GitHub Actions you can create robust deployments and testing scenarios for machine learning operations. Learn Objectives In this video lesson, I'll go over the details with an example repository you can use for reference including the following learning objectives: Create a FastAPI application with HuggingFace Interact with the model with HTTP from a container using FastAPI Containerize the application using GitHub Actions Create repository secrets to login and push to GitHub Container Registry (ghcr.io) Resources Example repository Practical MLOps book MLOps Maturity Model Packaging ML models.

Abstract: Cutting Edge MLOps from Zero with Hugging Face and OpenAI Learn by doing This video course takes you from zero all the way to building solutions using MLOps. First the theory of MLOps is explained, then you will learn how to build a MLOps style solution from scratch using Hugging Face and OpenAI. You will also learn to use AI tools like Github Copilot, Hugging Face Spaces, and OpenAI Codex. Learning Objectives Learn to use Github Codespaces and customize your environment with devcontainer.json Learn the foundations of MLOps Explore new topics in the upcoming book Enterprise MLOps by O'Reilly Learn to use Github Actions to test AI projects Learn to use Github Copilot to write code for both Hugging Face and OpenAI Learn to build AI solutions using GPT-3 Additional Popular Resources Pytest Master Class AWS Solutions Architect Professional Course Github Actions and GitOps in One Hour Video Course Jenkins CI/CD and Github in One Hour Video Course AWS Certified Cloud Practitioner Video Course Advanced Testing with Pytest Video Course AWS Solutions Architect Certification In ONE HOUR Python for DevOps Master Class 2022: CI/CD, Github Actions, Containers, and Microservices MLOPs Foundations: Chapter 2 Walkthrough of Practical MLOps Learn Docker containers in One Hour Video Course Introduction to MLOps Walkthrough AZ-900 (Azure Fundamentals) Quick reference guide 52 Weeks of AWS Episode 8: Infrastructure as Code with CDK and AWS Lambda Learn GCP Cloud Functions in One Hour Video Course Python Devops in TWO HOURS! MLOps Platforms From Zero: DatMLOps Platforms From Zero: Databricks, MLFlow/MLRun/SKLearn AWS Machine Learning Certification In ONE HOUR Fast, documented Machine Learning APIs with FastAPI Zero to One: AWS Lambda with SAM and Python in One Hour AWS Storage Solutions 2022: EBS/S3/EFS/Glacier Python Bootcamp for Data Testing In Python book Minimal Python book Practical MLOps book Python for DevOps-Playlist.