Kurzfassung: 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.

Kurzfassung: 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.

Kurzfassung: 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.

Kurzfassung: 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.

Kurzfassung: 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.

Kurzfassung: 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.

Kurzfassung: With demand for scaling, real-time access, and other capabilities, businesses need to consider building operational machine learning pipelines. This practical guide helps your company bring data science to life for different real-world MLOps scenarios. Senior data scientists, MLOps engineers, and machine learning engineers will learn how to tackle challenges that prevent many businesses from moving ML models to production. Authors Yaron Haviv and Noah Gift take a production-first approach. Rather than beginning with the ML model, you'll learn how to design a continuous operational pipeline, while making sure that various components and practices can map into it. By automating as many components as possible, and making the process fast and repeatable, your pipeline can scale to match your organization's needs. You'll learn how to provide rapid business value while answering dynamic MLOps requirements. This book will help you: Learn the MLOps process, including its technological and business value Build and structure effective MLOps pipelines Efficiently scale MLOps across your organization Explore common MLOps use cases Build MLOps pipelines for hybrid deployments, real-time predictions, and composite AI Learn how to prepare for and adapt to the future of MLOps Effectively use pre-trained models like HuggingFace and OpenAI to complement your MLOps strategy.

Kurzfassung: 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.

Kurzfassung: 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.

Kurzfassung: 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.

Kurzfassung: 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...

Kurzfassung: 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.

Kurzfassung: 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.

Kurzfassung: 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.

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

Kurzfassung: Switch to Rust Switching to Rust from Python (Live Rough Draft Series) This video series covers how to switch to Rust from Python. It is in rough draft format and will be updated as the series progresses. The aim is to get this content out to the community as soon as possible. You can follow along with the code on GitHub at * Rust Tutorial- https://nogibjj.github.io/rust-tutorial/ . * Rust MLOps Template- https://github.com/noahgift/rust-mlops-template Lessons Covered Include: Lesson 1: Introduction to Leveling Up from Python to Rust The lesson is an introduction to a "Switch to Rust" course that covers the use of a live book of Rust lecture notes, installation and setup of Rust on AWS Cloud9, using GitHub Copilot for Rust development, and leveling up from Python to Rust. It also compares the energy efficiency of Python and Rust. 1.0 Introduction to Switch to Rust Course 1.1 Live Book of Rust Lecture Notes Overview 1.2 Rust Installation and Setup on AWS Cloud9 and explain why different than Python 1.3 Using GitHub Copilot to assist in Rust development and Leveling Up from Python. 1.4 Leveling up rust packaging actix microservice and crates 1.5 Energy efficiency of Python vs Rust Lesson 2: Building High Performance Rust Command Line Interfaces for MLOps, Distributed Systems, and Data Engineering This lesson describes the use of Rust for high performance Machine Learning Operations (MLOPs) with Hugging Face, including a comparison of systems programming in Rust and Python. It covers the creation of a high-performance data engineering tool in Rust that deduplicates data using Rayon for safe and memory efficient concurrency. The lesson also discusses building a high-performance GPU-accelerated Hugging Face Translator CLI using Rust. 2.0 High Performance MLOPs with Rust Hugging Face doing Zero Shot Classification 2.1 systems programming rust vs python for MLOPs 2.2 Building a high-performance data engineering tool with Rust that deduplicates data using Rayon with safe and memory efficient concurrency 2.3 Building a high-performance GPU-accelerated Hugging Face Translator CLI with Rust Lesson 3: Building GPU-accelerated MLOps with Rust In this lesson, we discuss the setup of reproducible GitHub Codespaces for GPU workflows using Stable Diffusion with Rust PyTorch bindings. Next we highlight the use of Amazon Firecracker in AWS Lambda as one of the most impressive use cases and go on to explain how to train models in Rust using PyTorch that target the GPU. The lesson also covers the creation of a CUDA-enabled stress test using Rust PyTorch bindings and Rayon for multithreaded GPU-accelerated MLOps 3.0 Reproducible GitHub Codespaces repo setup for GPU workflows with Stable Diffusion with Rust PyTorch bindings 3.1 In this video, I talk about one of the most impressive use cases, amazon #firecracker that powers #aws #lambda. Can you get bigger than this? Then I dive into training models in Rust with #pytorch that target the GPU. 3.2 Building a CUDA-enabled Stress Test with Rust PyTorch bindings and Rayon for multithreaded GPU-accelerated MLOps Lesson 4: Building Rust Containerized Solutions Lesson 5: Building Rust Cloud Computing Solutions for AWS 5.1 Building an Actix Web Server with Rust and Deploying to AWS App Runner Learning Objectives Learn program in Rust using Python as a reference and advanced tools like GitHub Copilot to assist Learning to build solutions with Rust including CLI, Web, and MLOps Learning to build Rust solutions for AWS, GCP, and Azure Learning to build Rust solutions for Kubernetes, Docker, and Serverless Learning to build Rust solutions for Data Engineering, Data Science, and Machine Learning Operations (MLOps) Learning to build Rust solutions for GPU-accelerated Machine Learning Learning the key syntax and features of the Rust 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.

Kurzfassung: 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.

Kurzfassung: 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.

Kurzfassung: 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.

Kurzfassung: 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.

Kurzfassung: 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.

Kurzfassung: 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.

Kurzfassung: 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.

Kurzfassung: 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.

Kurzfassung: 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.

Kurzfassung: 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.

Kurzfassung: 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.

Kurzfassung: Many organizations today have begun to modernize their Windows workloads to take full advantage of cloud economics. If you're a C# developer at one of these companies, you need options for rehosting, replatforming, and refactoring your existing .NET Framework applications. This practical book guides you through the process of converting your monolithic application to microservices on AWS. Authors Noah Gift, founder of Pragmatic AI Labs, and James Charlesworth, engineering manager at Pendo, take you through the depth and breadth of .NET tools on AWS. You'll examine modernization techniques and pathways for incorporating Linux and Windows containers and serverless architecture to build, maintain, and scale modern .NET apps on AWS. With this book, you'll learn how to make your applications more modern, resilient, and cost-effective. Get started building solutions with C# on AWS Learn DevOps best practices for AWS Explore the development tools and services that AWS provides Successfully migrate a legacy .NET application to AWS Develop serverless .NET microservices on AWS Containerize your .NET applications and move into the cloud Monitor and test your AWS .NET applications Build cloud native solutions that combine the best of the .NET platform and AWS.

Kurzfassung: Learn to build solutions with Go and the AWS Cloud9 environment via the AWS SDK

Kurzfassung: A walkthrough of building a basic phrase repeater console application in VS Code 2022, .NET 6 and C# 10

Kurzfassung: Build a robust Python CLI tool with argparse The argparse module is part of the Python standard library and meant to help you build robust command line tools. Once you need flags, switches, values, or positional arguments, it is very difficult to achieve that without using a framework. In this case, argparse can help build all you need to handle inputs in the terminal. We'll see how a single file named size.py that calculates file sizes can make use of argparse to handle the input in the terminal. Then, we will cover how error handling is done automatically when the expectations from argparse aren't met. And finally, we'll go through some of the help menu options that construct the body of the help output. Check the resources section for the GitHub repo with sample code Why would you want to use argparse Since argparse is part of the Python standard library, there is no need for you to define or install any external dependencies. You are ensuring that any Python installation in any system will work correctly out of the gate. A very basic Python CLI tool without dependencies using the argparse module Next steps Now that you know how to enhance a Python script into a more powerful one with argparse , you can try out other frameworks like the Click framework or something much simpler by using the sys module. I have examples and guides for the sys.argv module and for using the Click framework . Useful links and resources GitHub repo with sample code Build Python applications from scratch Setup your Data Science environment with VSCode

Kurzfassung: Learn to perform Continuous Delivery of Container Based Projects using ECR, Cloud9 and App Runner. 00:00 Intro 00:27 Architectural Overview 01:30 FastAPI project overview 01:50 Setup AWS Cloud9 03:20 Setup ECR 05:00 Setup AWS App Runner

Kurzfassung: 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.

Kurzfassung: Learn to master MLOPs patterns with Databricks MLFlow as well as sklearn 00:00 Intro.

Kurzfassung: Swift Classes and Structures 00:00 Intro.

Kurzfassung: Learn about Swift Properties.

Kurzfassung: Learn to master Pytest via these tips and tricks 00:00 Intro 01:00 Key Concepts in Testing 10:00 Key Testing Terminology 15:00 Kazien equals DevOps 18:22 Setup Github 23:00 Setup Github Codespaces 25:00 Explaining Python pip freeze 33:00 Create Makefile 37:00 Pin requirements.txt 38:00 Cloud development environment concepts: AWS Cloud9 to Github Codespaces 43:00 Setup Github Actions 44:00 Configuring Matrix testing of Python 48:00 Building simple Python scripts as part of CI/CD and linting them with Pylint 56:00 Setup AWS CloudShell for CI/CD 01:00:09 Adding Python 3.7, 3.8, 3.9 and 3.10 to Matrix testing 01:04:00 Setup AWS Cloud9 01:11:00 Refactoring Python project to include testing directory and library directory 01:19:00 Setup Python Test Coverage 01:22:00 Adding Pytest to Github Actions YAML file 01:29:00 Creating Python library file that searches wikipedia and tests it 01:36:00 Adding Python Fire command-line tool 01:42:00 Running Pytests by search expression 01:44:30 Running Pytests by specifying tests 01:46:00 Marking tests in Pytest and avoiding slow tests 01:48:00 Profiling testing speed using Pytest 01:56:39 Doing distributed testing with xdist to spread tests to multiple cores 02:05:21 Created distributed testing group to distribute to multiple cores 02:09:00 Setup 32 Core 60GB RAM Cloud9 Machine to run massively parallel Pytests 02:13:58 Running distributed testing that doubles the speed of the tests 02:23:00 Setup Cloud-Native AWS Code Build testing with buildspec.yml that does distributed testing to 8 Core Build client.

Kurzfassung: 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.

Kurzfassung: 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.

Kurzfassung: Swift is a programming language that is designed to be a simple, yet powerful. 00:00 Intro 01:30 Diving into Type Casting 02:50 Type Casting in Swift 05:00 Type Casting in Swift with Example.

Kurzfassung: Learn to pass the AWS ML Certification Part 2 00:00 Intro 01:56 Artificial intelligence, machine learning, and deep learning. 02:34 Artificial Intelligence 03:10 Machine Learning 03:58 Deep learning 05:11 ML and technology advancements 06:00 Common ML Use Cases 08:14 Types of machine learning 09:21 Supervized learning 10:17 Computer vision 11:08 Unsupervized learning 12:44 Natural language processing 13:20 Reinforcement learning 14:34 Self-driving vehicles 15:04 When to use machine learning 15:35 ML pipeline: Business 16:23 ML pipeline: Data preparation 17:22 ML pipeline: Iterative model training 18:01 ML pipeline: Feature Engineering 19:25 ML pipeline: Model Training 20:18 ML pipeline: Evaluating and tuning the model 21:00 Overfitting and underfitting 22:48 Python tools and libraries 24:57 ML Frameworks 25:50 Amazon SageMaker 26:42 Machine learning managed services 28:30 Machine learning challenges.

Kurzfassung: 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.

Kurzfassung: Learn Key Skills in Software Engineering Craftmanship Course An Ongoing Course This video will show you how to use CUDA GPUs with Python. Lessons Covered Include: Introduction to course What is a Makefile and how do you use it? Popular Linux commands Configuring ~/.bashrc Using grep, cut, sort and uniq Introducing GitHub Codespaces Compiling Python in Github CodeSpaces What is DevOps and how do you use it? Kaizen Methodology Learn Continuous Integration Learn key software engineering principles and best practices for building software. Learn both concepts and hands-on techniques for building software. Learn software craftsmanship. 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.

Kurzfassung: 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.

Kurzfassung: 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.

Kurzfassung: 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.

Kurzfassung: 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.

Kurzfassung: 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.

Kurzfassung: 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.

Kurzfassung: 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.

Kurzfassung: MLOps is consistently one of the greatest challenges engineers face when creating and maintaining machine learning systems. Join expert practitioners to learn techniques and best practices for operationalizing machine learning models and explore case studies of them in action, showing you what works--and what doesn't. What you'll learn and how you can apply it Understand MLOps processes for model deployment, containerization, and automation as well as monitoring, continuous experimentation, and improvement Learn how an understanding of SRE and DevOps principles can enhance the practice of MLOps Avoid common pitfalls in the process of building end-to-end machine learning pipelines This recording of a live event is for you because... You're a data or machine learning practitioner who puts machine learning models into production, or you're embarking on an MLOps career path. You want to improve your process of productionizing machine learning models by applying new techniques and best practices. Recommended follow-up: Read Practical MLOps (book) Read Reliable Machine Learning (book) Watch Radar Talks: Hugo Bowne-Anderson on MLOps Versus DevOps (video) Take Practical MLOps (live online course with Noah Gift) Take Open Source MLOps in 4 Weeks (live online course with Alex Kim) Read Site Reliability Engineering (book).

Kurzfassung: 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.

Kurzfassung: Assimilate Typescript Learning Typescript During Live Coding This video series covers live coding the Typescript Language iteratively thus learning the language. Lessons Covered Include: 1.0 Setup Typescript in GitHub Codespaces compile and run hello world examples in the browser and with Node.js Learning Objectives Learn Typescript Learning to build solutions with Typescript Learning the key syntax and features of the Typescript 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.

Kurzfassung: Assimilate Containers Learning Containers During Live Coding This video series covers live coding Containers iteratively while learning the concepts. Lessons Covered Include: 1.0 ep1 getting started containers build tiny bash container Learning Objectives Learn Containers Learning to build solutions with Docker, Containers and Kubernetes Learning the key syntax and features of Containers 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.

Kurzfassung: 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.

Kurzfassung: 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.

Kurzfassung: Build a very simple command line tool with Python without using external dependencies or frameworks. In this video you'll see how with just a few lines of Python you can transform a file into a script with a help menu. All by using the sys.argv module and a simple if statement. Although you can create more powerful command-line tools with other frameworks like argparse or external libraries like Click, you can still implement a quick script without them that includes a help menu Useful links: Github repo with sample code Introduction to Python

Kurzfassung: Step up your local setup by setting a remote compute instance with attached SMB storage and then connect your local VSCode editor so that you can execute code and Jupyter Notebooks remotely. In this video I go through everything you need to know to crete a compute instance in Azure, both in Azure Machine Learning Studio and using the Data Science Virtual Machine spot instance that allows you to save costs when running a high memory with GPU instance. Because remote compute instances in the cloud should be considered ephemeral, specially if you are using spot instances, this video also covers setting up a storage account and a file share configuration for a Samba share that allows you to attach that same storage to any compute. Topics include: Create an Azure compute instance Learn about Spot Instances and other cost savings Setup VSCode for remote code execution Create an SMB file share and attach it to a VM Execute Jupyter Notebooks remotely Useful links: * Github Repository with summary instructions * Create a File share documentation on Azure * Data Science VMs in Azure

Kurzfassung: Find out how to share and use Google Colab notebooks for data science!

Kurzfassung: 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.

Kurzfassung: 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.

Kurzfassung: 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.

Kurzfassung: 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.

Kurzfassung: 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.

Kurzfassung: 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.

Kurzfassung: 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.

Kurzfassung: 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.

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

Kurzfassung: 52 Weeks of Live Coding MLOps-The Series Live Coding on New and Exotic MLOps Tools Weekly This is a weekly series of live coding sessions on new and exotic MLOps tools. Live Coding Sessions Include: 1.0-52-Weeks-Live-Coding-MLOps-Episode-1-mlops-with-tensorflow 2.0-52-Weeks-Live-Coding-MLOps-Episode-2-Serving TensorFlow Models 3.0-52-Weeks-Live-Coding-MLOps-Episode-3-Serving Tensforflow with TensorFlow Serving 4.0-52-Weeks-Live-Coding-MLOps-Episode-4-Live Coding Hugging Face 5.0-52-Weeks-Live-Coding-MLOps-Episode-5-More Hugging Face 6.0-52-Weeks-Live-Coding-MLOps-Episode-6-Even More Hugging Face 7.0-52-Weeks-Live-Coding-MLOps-Episode-7-Open AI with Github Codespaces 8.0-52-Weeks-Live-Coding-MLOps-Episode-8-Live Coding Ludwig 9.0-52-Weeks-Live-Coding-MLOps-Episode-9-Diving into Ludwig 10.0-52-Weeks-Live-Coding-MLOps-Episode-10-mlrun 11-52-Weeks-Live-Coding-MLOps-Sagemaker-Studio-Lab Topics Covered Include: Enterprise MLOps MLOps MLOps on AWS MLOps on GCP MLOps on Azure MLOps and DevOps MLOps with Ludwig MLOPs with mlrun MLOPs with Sagemaker Studio Lab 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.

Kurzfassung: 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.

Kurzfassung: 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.

Kurzfassung: 52 Weeks of .NET Learning .NET and C# by Live Coding This video series live coding .NET and iteratively learning the language. Lessons Covered Include: 1.0 52 Weeks of Live Coding NET Episode 1 codespaces.mp4 2.0 52 Weeks of Live Coding NET Episode 2 Xunit.mp4 3.0 52 Weeks of Live Coding NET Episode 3 Blazor.mp4 4.0 52 Weeks of Live Coding NET Episode 4 failing to get cdk1 working.mp4 5.0 52 Weeks of Live Coding NET Episode 5 success cdk2.mp4 Learning Objectives Learn .NET and C# and F# 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.

Kurzfassung: 52 Weeks of EIL (Exploring Interesting Languages) Learning Interesting Languages Live Coding This video series live coding languages iteratively thus learning the language. Lessons Covered Include: 1.0 52 weeks of exploring interesting languages Episode 1 Julia installing getting started 2.0 52 weeks of exploring interesting languages Episode 2 Julia strings 3.0 52 weeks of exploring interesting languages Episode 3 Julia functions 4.0 52 weeks of exploring interesting languages Julia Episode 4 Julia piping vectorizing functions 5.0 52weeks EIL Julia EP5 Control Flow Learning Objectives Exploring interesting languages and frameworks Learning to code in Julia 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.

Kurzfassung: 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.

Kurzfassung: 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.

Kurzfassung: 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.

Kurzfassung: Learn the Apple Swift Languages by using the XCode Playground environment.

Kurzfassung: Building AI APIs with .NET 6 and AWS Comprehend

Kurzfassung: Learn to build an S3 Bucket Lister Console app using VS 2022 and AWS targeting .NET 6.

Kurzfassung: Learn to use AWS Data Governance and Auditing Tools.

Kurzfassung: Demo of FastAPI + AWS App Runner

Kurzfassung: Learn to deploy an AWS Lambda Function in Python with CDK. 00:00 Intro 00:36 Verify CDK Version 01:00 Initialize Python CDK app 02:09 Create AWS Python Lambda 02:50 Add AWS CDK Stack for AWS Lambda 03:42 Run cdk diff 04:06 bootstrap cdk 05:31 run cdk deploy result 06:01 test AWS Lambda in Console

Kurzfassung: Perform Data Science on Day One! 00:00 Intro 02:02 Data Science Project Structure Overview 05:15 Create Github Repo 07:28 Launch Github Codespaces 11:34 Using Colab Notebook 12:47 Using TOC in Colab Notebook 14:43 Saving notebook to Colab to Github 19:52 Ingesting CSV files into Colab 22:31 Describing Data using df.describe() 24:30 Plotting data with seaboard 28:30 lmplot 29:30 Comparing cumulative deaths in Covid19 plot by state 32:40 Merging Pandas Dataframe with election and Sugar Consumption 37:38 Exporting CSV file and uploading to Github from Colab result 39:27 Continuous Integration of Jupyter Notebook with Github Actions 40:44 Create Makefile 44:32 Using Github Actions to test Jupyter via nbval plugin 45:43 Using Github Status Badge for Jupyter Notebook test run pass

Kurzfassung: 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?.

Kurzfassung: 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.

Kurzfassung: Learn functions in Swift 00:00 Intro 03:00 Calling functions 06:00 Multiple parameters 21:00 Nested functions.

Kurzfassung: 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.

Kurzfassung: 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.

Kurzfassung: 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.

Kurzfassung: 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.

Kurzfassung: Learn to pass the AWS ML Certification 00:00 Intro 03:00 AWS ML Certification Course Outline 05:21 What is the Data Scientist role? 05:58 Machine Learning engineer 06:33 Applied science researcher 07:08 Machine learning developer role 08:05 Resources and documentation 10:48 AWS SageMaker Studio Lab 13:18 AWS Machine Learning Lens.

Kurzfassung: 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.

Kurzfassung: GitHub Copilot Driven: Python DevOps from Functions to Continuous Delivery of Microservices on AWS Learn to use Python for DevOps and build a GitHub copilot driven microservice on AWS Noah Gift is the author of a course on Python for DevOps using GitHub Copilot. He explains how to deal with the file system, how to set up continuous integration, and how to build command line tools that help you automate your workflow. Noah explains some of the tools for building DevOps workflows on GitHub and wraps the course up with a FastAPI Microservice deployed on AWS using AWS App Runner. Topics Covered Include: Containers GitHub Actions GitHub Copilot GitHub Codespaces AWS App Runner Containers FastAPI Continous Integration and Continous Delivery Python DevOps DevOps Skills with the Python Ecosystem 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.

Kurzfassung: 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.

Kurzfassung: Kubernetes for Data Learning Kubernetes For Data This video series covers the Kubernetes ecosystem for data. Lessons Covered Include Six Key Lessons: 1.0 What is Kubernetes? 1.1 Cluster Architecture 1.2 Pods and Nodes 1.3 Services and Deployments 2.0 Running Kubernetes with Minikube 3.0 3.0 4.0 5.0 1.1 Docker Container Demo 1.2 Minikube Demo 1.3 Minikube Dashboard 1.4 Deploying a containerized FastAPI App with Kubernetes Learning Objectives Learn Kubernetes for Data Learning to build solutions with Kubernetes for Data Learning the key syntax and features of Kubernetes for Data 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.

Kurzfassung: Assimilate Go Learning Golang During Live Coding This video series covers live coding the Go Language iteratively thus learning the language. Lessons Covered Include: 1.0 Setup GitHub Codespaces and run hello world Go Learning Objectives Learn Go Learning to build solutions with Go Learning the key syntax and features of the Go 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.

Kurzfassung: Python for DevOps 2023: GitHub Codespaces CI/CD Microservice Pipeline Deployed to AWS App Runner and GCP Cloud Run Learning The Core Principles of Python for DevOps From Zero During Live Coding This video series covers live coding a step by step Python Microservice that serves out geopy requests that convert to wikipedia pages that get convereted to key phrases via a FastAPI microservice that is deployed to AWS App Runner and GCP Cloud Run. Lessons Covered Include: How to setup a GitHub Codespaces CI/CD Pipeline from Zero How to arrange Python libraries and test them How to containerize a Python Microservice How to setup continuous delivery using AWS Cloud9 and ECR How to integrate AWS Code Build to do a CI/CD Pipeline that deploys to AWS App Runner How to setup continuous delivery using GCP Cloud Shell and GCR Learning Objectives Learn Python for DevOps using live coding Learning to build solutions with Python and FastAPI and Containers. 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.

Kurzfassung: Assimilate PyTorch Learning PyTorch During Live Coding This video series covers live coding the PyTorch Language iteratively while learning the language. Lessons Covered Include: 1.0 Getting started with PyTorch in GitHub Codespaces with GPU (Live Coding) Learning Objectives Learn PyTorch Learning to build solutions with PyTorch Learning the key syntax and features of the PyTorch Framework 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.

Kurzfassung: Assimilate GitHub Learning GitHub During Live Coding This video series covers live coding GitHub iteratively thus learning the platform. Lessons Covered Include: 1.0 ep1 getting started with GitHub Codespaces Actions Learning Objectives Learn GitHub Learning to build solutions with GitHub Learning the key technologies of GitHub like Actions, Codespaces, and more 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.

Kurzfassung: Assimilate Haskell Learning Haskell During Live Coding This video series covers live coding the Haskell Functional Programming Language iteratively thus learning the language. Lessons Covered Include: 1.0 Getting started with Haskell in GitHub Codespaces Learning Objectives Learn Haskell Learning to build solutions with Haskell Learning the key syntax and features of the Haskell 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.