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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Abstract: Microsoft Azure Fundamentals (AZ-900) Certification Pass the Azure fundamentals certification This early release and ongoing course will give you everything you need to pass the Azure Fundamentals (AZ-900) certification. This is the foundational certification for Azure which is essential for working in Microsoft's cloud. The course will go into every content that is relevant to pass the certification including how to create a study guide and strategy to pass the exam. Course overview This course contains a complete walkthrough over all of the content relevant for the AZ-900 certification with a summary and specialized notes after each sections, following the same content that is available as Learning Paths. Aside from the video content, create your own study notes and strategy using the example reference guide This course includes all of the author's study notes so that you can use them in addition to your own notes. Course Content Describe Cloud Concepts Cloud Concepts Study notes Introduction to Azure Fundamentals Discuss Azure concepts Describe code Azure Architectural components About your instructor Alfredo Deza has over a decade of experience as a Software Engineer doing DevOps, automation, and scalable system architecture. Before getting into technology he participated in the 2004 Olympic Games and was the first-ever World Champion in High Jump representing Peru. He currently works in Developer Relations at Microsoft and is an Adjunct Professor at Duke University. This solid background in technology and teaching, including his experience studying and passing the AZ-900 is seen throughout this course, where you will get a first-hand experience with study notes, example repositories, and a solid study strategy. Resources Study notes Quick AZ-900 reference guide Azure Remote Compute for VSCode Automated Azure Resource Cleanup Azure SAMBA file share Learn Azure (AutoML) in one hour Deploying containers to Azure Practical MLOps book.

Abstract: Azure in GitHub Actions Authenticate to Azure with GitHub Actions If you need to use GitHub Actions and interact with Azure resources, you'll need to authenticate properly. Authenticating is made easy by direct Action support in a workflow, but it requires a Service Principal created in a specific way that produces output that is required to go into a GitHub Secret. Using a sample GitHub repository, the video lesson will cover all the details you need to know to get a working GitHub workflow step to authenticate to Azure and then use the Azure CLI to perform operations. Learning Objectives In this lesson you will learn: Create a GitHub Action workflow to login to Azure Create an Azure Service Principal with enough permissions Add a GitHub Actions Secret from the Service Principal Run the workflow that uses the Azure CLI Useful Resources Sample GitHub Repository GitHub Actions and GitOps in One Hour Run Python in GitHub Actions Azure Service Principal documentation How to install the Azure CLI Free Azure Certification for Students.

Abstract: Learn to install Python from scratch with Github Codespaces. Also learn to use Github Copilot with Ludwig. 00:00 Intro 01:09 Create new Github Repository 01:58 Create new Github Codespace with 16 cores 02:62 Configure Visual Studio Code Dark Theme 03:47 apt-get update and apt-get install python development tools 04:32 Using htop to monitor multi-core load 05:13 downloading python from www.python.org 05:45 Installing python from source 06:40 ./configure --enable-optimizations 07:28 Compiling python with multi-core flag make -j 16 09:24 Finishing install with sudo make altinstall 10:07 Replacing system python by using the newly installed python with ~/.bashrc alias 11:07 Creating virtualenv and sourcing it inside of ~/.bashrc 12:22 Create project scaffold with requirements.txt and Makefile 12:00 Installing ludwig with make install command 13:53 Install Github Copilot 14:29 Commit code and setup Github Actions 17:41 Test out Github Copilot auto-complete with "ludwig".

Abstract: Enterprise MLOps Interviews Learn Enterprise MLOps from the experts This video series interviews the experts at MLOps to learn how to use MLOps to build and deploy ML models. Interviews Include: Introduction GPT-3: O'Reilly authors Shubham Saboo and Sandra Kublik: I talk with the authors of the new O'Reilly book GPT-3 about a range of topics, including why they wrote the book. Conversation with Piero Molino and Ludwig/Predibase: Detailed conversation about Declarative AutoML with Piero Molino, author of Ludwig and co-founder Predibase. Asaf Somekh, CEO Iguazio: Talk at Duke MIDS MLOps Course: Life of a Model (or the brutal reality of applying ML in enterprises and how to deal with it). Javier Luraschi and Pedro Luraschi, Co-Founders of Hal9.ai: Discuss MLOps with Javascript, including no-code and low-code approaches and Tensorflow.js. Topics Covered Include: Enterprise MLOps MLOps MLOps on AWS MLOps on GCP MLOps on Azure MLOps and DevOps MLOps with Ludwig Additional Popular Resources Pytest Master Class AWS Solutions Architect Professional Course Github Actions and GitOps in One Hour Video Course Jenkins CI/CD and Github in One Hour Video Course AWS Certified Cloud Practitioner Video Course Advanced Testing with Pytest Video Course AWS Solutions Architect Certification In ONE HOUR Python for DevOps Master Class 2022: CI/CD, Github Actions, Containers, and Microservices MLOPs Foundations: Chapter 2 Walkthrough of Practical MLOps Learn Docker containers in One Hour Video Course Introduction to MLOps Walkthrough AZ-900 (Azure Fundamentals) Quick reference guide 52 Weeks of AWS Episode 8: Infrastructure as Code with CDK and AWS Lambda Learn GCP Cloud Functions in One Hour Video Course Python Devops in TWO HOURS! MLOps Platforms From Zero: DatMLOps Platforms From Zero: Databricks, MLFlow/MLRun/SKLearn AWS Machine Learning Certification In ONE HOUR Fast, documented Machine Learning APIs with FastAPI Zero to One: AWS Lambda with SAM and Python in One Hour AWS Storage Solutions 2022: EBS/S3/EFS/Glacier Python Bootcamp for Data Testing In Python book Minimal Python book Practical MLOps book Python for DevOps-Playlist.

Abstract: Assimilate Google Machine Learning Certification Learn Google Machine Learning Certification This video course will show you how to learn Google Machine Learning Certification material to pass the exam. Topics Covered Include: 1.0 Introduction to OpenAI, a one hour exploration of many of the cutting edge AI models and projects you can build in the playground. 2.0 Assimilate Google Professional ML Certification EP2 problem framing heuristics and recommendation engines Learning Objectives Learn to master ML with the Google Machine Learning Certification course Learn to master ML with the Google Cloud Platform Additional Popular Resources 52 Weeks of AWS-The Complete Series Pytest Master Class AWS Solutions Architect Professional Course Github Actions and GitOps in One Hour Video Course Jenkins CI/CD and Github in One Hour Video Course AWS Certified Cloud Practitioner Video Course Advanced Testing with Pytest Video Course AWS Solutions Architect Certification In ONE HOUR Python for DevOps Master Class 2022: CI/CD, Github Actions, Containers, and Microservices MLOPs Foundations: Chapter 2 Walkthrough of Practical MLOps Learn Docker containers in One Hour Video Course Introduction to MLOps Walkthrough AZ-900 (Azure Fundamentals) Quick reference guide 52 Weeks of AWS Episode 8: Infrastructure as Code with CDK and AWS Lambda Learn GCP Cloud Functions in One Hour Video Course Python Devops in TWO HOURS! MLOps Platforms From Zero: DatMLOps Platforms From Zero: Databricks, MLFlow/MLRun/SKLearn AWS Machine Learning Certification In ONE HOUR Fast, documented Machine Learning APIs with FastAPI Zero to One: AWS Lambda with SAM and Python in One Hour AWS Storage Solutions 2022: EBS/S3/EFS/Glacier Python Bootcamp for Data Testing In Python book Minimal Python book Practical MLOps book Python for DevOps-Playlist.

Abstract: Learn to master DevOps with the Python Language 00:00 Intro 02:12 What is DevOps? 05:00 Create Github Repo 18:00 Statements in Python using Colab 30:32 Create Python Scaffold using Github Codespaces 37:00 Create Python Virtualenv and add to ~/.bashrc 40:53 Launch AWS CloudShell and checkout code 42:09 Launch AWS Cloud9 46:00 Freezing packages with pip freeze 55:00 Setup Github Actions 01:14:31 Test code with Github Actions and Pytest 01:23:00 Build CLI with Python and Python Fire 01:25:52 Build Python Lambda Functions 01:40:00 Build AWS Step Functions 01:43:00 Build Python Fire Step Functions 01:57:00 Build Containerized Microservice with FastAPI and AWS App Runner.

Abstract: Python Bootcamp for Data Learn foundational Python with an emphasis for data In this course, you will learn how to work effectively with Python. You will understand how to use variables, create functions, and work with classes. That foundational knowledge will allow you to understand testing and testing techniques to validate your work and then move onto Pandas and Numpy which allows you to work effectively with data sets and other data science tasks. This is valuable for anyone wanting to get a quick introductory course on Python, like a student, programmer new to Python or aspiring data engineer or data scientist. At the end of this course you'll be ready to work with more advanced concepts with Pandas and Numpy with a solid foundation in Python for any other task. All lessons and videos have accompanying GitHub Repositories with example code. Learn Objectives This course has extensive content that covers Python for beginners and then moves onto more complex Python operations including data analysis, exploration, and manipulation with Pandas and NumPy. It will include the following learning objectives: Work with logic in Python, assigning variables and using different data structures Create functions and classes of different types Write, run, and debug tests using Pytest to validate your work Manipulate data with Pandas Create and modify NumPy arrays Index This course is divided into content for 4 weeks, with 3 lessons per week: Week 1: Introduction to Python Reference GitHub Repository Working with variables and types Introduction to Python data structures Adding and extracting data from data structures Week 2: Python functions and Classes Reference GitHub Repository Working with functions Building classes and using methods Modules and advanced usage Week 3: Testing in Python Reference GitHub Repository Introduction to testing Writing useful tests Test failures Week 4: Introduction to Pandas and Numpy Reference GitHub Repository Basic Pandas usage Working with datasets Introduction to NumPy Resources Week 1 GitHub repository: Introduction to Python Week 2 GitHub repository: Python Functions and Classes Week 3 GitHub repository: Testing In Python Week 4 GitHub repository: Introduction to Pandas and Numpy Python dictionaries Learn Module Testing In Python book Minimal Python book Python for Beginners Learn Path Practical MLOps book.

Abstract: Assimilate Databricks ML Certification Learn to pass Databricks ML Certification weekly by example This video course will show you how to understand the Databricks ML Certification exam and how to pass it. Topics Covered Include: 1.0 Getting started with Databricks and the ML Certification using Azure 2.0 Introduction to using Databricks clusters, the interface, notebooks and the Pandas API for Spark Learning Objectives Learn Databricks ML Certification material Learn Databricks clusters, notebooks and the Pandas API for Spark Additional Popular Resources 52 Weeks of AWS-The Complete Series Pytest Master Class AWS Solutions Architect Professional Course Github Actions and GitOps in One Hour Video Course Jenkins CI/CD and Github in One Hour Video Course AWS Certified Cloud Practitioner Video Course Advanced Testing with Pytest Video Course AWS Solutions Architect Certification In ONE HOUR Python for DevOps Master Class 2022: CI/CD, Github Actions, Containers, and Microservices MLOPs Foundations: Chapter 2 Walkthrough of Practical MLOps Learn Docker containers in One Hour Video Course Introduction to MLOps Walkthrough AZ-900 (Azure Fundamentals) Quick reference guide 52 Weeks of AWS Episode 8: Infrastructure as Code with CDK and AWS Lambda Learn GCP Cloud Functions in One Hour Video Course Python Devops in TWO HOURS! MLOps Platforms From Zero: DatMLOps Platforms From Zero: Databricks, MLFlow/MLRun/SKLearn AWS Machine Learning Certification In ONE HOUR Fast, documented Machine Learning APIs with FastAPI Zero to One: AWS Lambda with SAM and Python in One Hour AWS Storage Solutions 2022: EBS/S3/EFS/Glacier Python Bootcamp for Data Testing In Python book Minimal Python book Practical MLOps book Python for DevOps-Playlist.

Abstract: Python Command Line Tools Course This is a full Python CLI course that will take you from the most basic approaches to command line tools in Python to the more advanced featuers that use frameworks like argparse and Click. Everything on this course is available in the course Repository on GitHub, so that you can follow the examples closely and use that as a foundation with all examples and sample files. Building a CLI is the foundation for automation in your daily work By the end of the course you should feel confident in creating a tool, and the following: Create simple CLI tools without any frameworks Learn about arguments, flags, help menus and how to create them automatically Use the argparse framework to build more complex tools Build a CLI with the Click framework Use special features of Click like colored output and argument types Modularizing and project scaffolding in Python Packaging and packaging files in Python How to create tests and run them automatically Continuous Integration and Continuous Deployment with Github Actions for Python Packaging a CLI tool with an executable Distributing a CLI tool on PyPI (the Python Package Index) How to automatically test your project on a PR (GitHub pull request) How to automate publishing your tool to PyPI on a release We'll cover 3 different ways to create tools, from the very basic (with sys.argv) to using a framework that comes with Python ( argparse), and finally the more involved, by using an external library like Click. This flexibility is essential because it will let you decide when to use a simple single file with no dependencies to a fully featured framework like Click. Then you will learn about the project layout, modularizing and organizing your code in directories and files while keeping everything useful for a CLI. You will test your code using Pytest and then package your code using Python packaging files and tools. You will continue by automating everything, including the testing and linting using GitHub Actions, and finally publishing your project to the Python Package Index so that it can be installed by anyone in the world. Course Resources Course Repository on GitHub Build Python applications from scratch.

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

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

Abstract: Learn to build a project from zero, test it with Github Actions. Next, build both Click and Fire Command-Line Tools. Finally containerize a Fast API Wikipedia Scraper and deploy to AWS App Runner. 00:00 Intro 03:00 Statements in Python using Colab 10:00 List and Dictionaries Python 21:00 Using Github Codespaces to build project 38:00 Setup Github Actions 44:00 Python Functions 1:03:00 Build Wikipedia bot 1:12:00 Build Click Command-Line Tool Interface Wikipedia Bot 1:38:15 Build Python Fire Command-Line Tool 1:42:21 Build FastAPI Wikipedia Bot 1:56:00 Containerize FastAPI Wikipedia Bot with Docker 2:07:08 Deploy AWS App Runner FastAPI Wikipedia Bot with Docker.

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

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

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

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

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

Abstract: 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.

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

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

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

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

Abstract: Learn about Swift Properties.

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

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

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

Abstract: Learn to 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.

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

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

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

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

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

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

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

Abstract: 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.

Abstract: Intro to Pandas Over 1 hour of course material with practice code Quickly learn to use Pandas with examples Learn how to work with data using Pandas and NumPy. From loading and reading datasets from different sources to plotting graphs and exploring common problems in data. Pandas will allow you to perform transformations and export your data into different formats, and NumPy will boost your ability to work with numerical data. This course is meant for beginners that want to understand Pandas from the start and find more about NumPy. All lessons and videos have accompanying GitHub Repositories with example code. Reference GitHub Repository Learning Objectives In this course you will learn to: Load and export data from different sources Manipulate data in datasets Apply functions and transform columns Query for specific data Perform common operations on NumPy arrays Resources Python for beginners course Python Functions and Classes course Python Dictionaries course Introduction to Testing in Python course Pytest Master Class Python Bootcamp for Data Practical MLOps book.

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

Abstract: 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.

Abstract: GitHub Codespaces Course A full introduction to Codespaces with practical examples This course will show you everything you need to know to be productive using GitHub Codespaces , a service that allows you to run a configurable development environment with one-click launch with a remote compute in the cloud. Being able to run Visual Studio Code in the cloud for any project with a pre-configured environment is going to allow you to concentrate on the learning if you are student, the teaching if you are a professor, or being plain productive if you are already a technologist. Learning Objectives This course goes through some of the basics of using default Codespaces environments, but it also goes into configuring environments to take advantage of its best features, including: Learning about Codespaces uses and how it fits for learning, teaching, or working in projects Running default Codespaces and configuring it in your GitHub account Configuring Codespaces for different development environments Applying practical examples in Codespaces environments using Docker, Python, and many other tools About your instructor Alfredo Deza has over a decade of experience as a Software Engineer doing DevOps, automation, and scalable system architecture. Before getting into technology he participated in the 2004 Olympic Games and was the first-ever World Champion in High Jump representing Peru. He currently works in Developer Relations at Microsoft and is an Adjunct Professor at Duke University. This solid background in technology and teaching, including his experience creating courses with Codespaces is seen throughout this course, where you will get a first-hand experience with practical examples as well as applicable configuration for any development environment. Resources Python Bootcamp Pytest Master Class Practical MLOps book What is GitHub Codespaces Azure Codespaces Template.

Abstract: 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.

Abstract: Azure PostgreSQL Setup and deploy an Azure PostgreSQL database Learn how to setup a PostgreSQL database using the Azure cloud. From the different options you have to create a PostgreSQL database (flexible or single server) to connection strings and firewall settings. This video will walk you through all you need to know to setup the resources, understand the constraints, and then correctly connect from a remote workstation using psql . Learning Objectives In this video you will learn to: Use the Azure Portal to create a PostgreSQL database Select between single and flexible server options Understand the constraints to create a database Setup firewall settings for external access Use psql to connect from a remote workstation to PostgreSQL Use the default database to connect Resources Azure Fundamentals Certification Azure Fundamentals quick reference guide Azure remote compute for VSCode Automated Azure resource cleanup Learn Azure ML (AutoML) in One hour.

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

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

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

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

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

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

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

Abstract: Assimilate OpenAI Learn OpenAI weekly by example This video course will show you how to build real-world applications with OpenAI's pre-trained cutting edge AI models. Topics Covered Include: 1.0- Introduction to OpenAI, a one hour exploration of many of the cutting edge AI models and projects you can build in the playground. Learning Objectives Learn the OpenAI platform Build a real-world application using OpenAI's pre-trained models Explore the types of projects you can build with OpenAI: Q&A, Summarization, NLP, SQL translation, Python to natural language, etc. Tweaking generated Python code from OpenAI inside of Github CodeSpaces and Cloud9 Additional Popular Resources Pytest Master Class AWS Solutions Architect Professional Course Github Actions and GitOps in One Hour Video Course Jenkins CI/CD and Github in One Hour Video Course AWS Certified Cloud Practitioner Video Course Advanced Testing with Pytest Video Course AWS Solutions Architect Certification In ONE HOUR Python for DevOps Master Class 2022: CI/CD, Github Actions, Containers, and Microservices MLOPs Foundations: Chapter 2 Walkthrough of Practical MLOps Learn Docker containers in One Hour Video Course Introduction to MLOps Walkthrough AZ-900 (Azure Fundamentals) Quick reference guide 52 Weeks of AWS Episode 8: Infrastructure as Code with CDK and AWS Lambda Learn GCP Cloud Functions in One Hour Video Course Python Devops in TWO HOURS! MLOps Platforms From Zero: DatMLOps Platforms From Zero: Databricks, MLFlow/MLRun/SKLearn * AWS Machine Learning Certification In ONE HOUR Fast, documented Machine Learning APIs with FastAPI Zero to One: AWS Lambda with SAM and Python in One Hour AWS Storage Solutions 2022: EBS/S3/EFS/Glacier Python Bootcamp for Data Testing In Python book Minimal Python book Practical MLOps book Python for DevOps-Playlist.

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

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

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

Abstract: 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.

Abstract: Learn to use the M1Ultra to train a deep learning model using Ludwig the declarative deep learning framework. 00:00 Intro 00:31 Checking out M1Ultra 64 Core GPU and 20 Core CPU 01:31 Launching Visual Studio Code with a virtualenv 01:49 Checking out Makefile for Ludwig install process on Mac M1Ultra 64 Core GPU and 20 Core CPU machine 03:27 Training the Ludwig model using PyTorch Deep Learning in 30 seconds. Rotten Tomatoes binary classification. 04:28 Checkout out model structure on disk.

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

Abstract: Assimilate 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.

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

Abstract: Learn to pass the AWS Solutions Architect Professional Exam. Hello, my name is Noah gift, and I'll be your instructor. This course is AWS Certified Solutions Architect or SAP, co one, a bit of my background. I've worked for approximately 25 years in technology, written multiple books, and I've been certified on multiple AWS exams, and helped develop exams as well as an SME. One of my goals is to teach you to pass the challenging Solutions Architect exam. Let's go through an overview. First up, we have one tool point, 5% in this domain, for organizational complexity. In domain two, we have designed new solutions; this is about a third of the exam. In domain three, we have migration planning 15% domain for cost control, which is about 12 and a half percent, and then domain five will be continuous improvement, or Kaizen is a term I like to use. Let's go ahead and take a look at some of these domains at a very, very high level. So first up, in terms of design for organizational complexity, you're going to need to know things like cross-account authentication, and access strategies, how to design networks, how to prepare for the multi-account; in domain two, you're going to cover security requirements controls when designing and implementing a solution also, about how to design and implement solutions. And then in in terms of business continuity, and performance objectives, those those are the things you're going to be taking care of. Now, in domain three, you'll talk about migration planning. So how do you take an existing workload, move it to the cloud, speak a few migration tools and cloud architecture for a current solution? In domain four, you're going to go into cost control. So what are the pricing models? What are some design controls? And also, how do you identify opportunities to reduce cost? In domain five, this would be a continuous improvement for existing solutions. So how to troubleshoot, maintain different troubleshooting strategies, develop performance metrics for applications and improve the security of existing solutions. So you can see there are a lot of domains here that we're going to cover and let's go ahead and get started. Domain 1: Design for Organizational Complexity Domain 2: Design for New Solutions Domain 3: Migration Planning Domain 4: Cost Control Domain 5: Continuous Improvement.

Abstract: Swift Classes and Structures 00:00 Intro.

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

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

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

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