Abstract: Learn Python and Pandas with O'Reilly Author Kennedy Behrman.

Abstract: Big Data Processing with Hadoop, Spark, Snowflake and Databricks. Learn to process big data using popular platforms like Hadoop, Spark, Snowflake and Databricks through live coding examples Learn from O'Reilly author Kennedy Behrman. This video series covers key concepts and tools for big data processing and storage. It introduces platforms like Hadoop, Spark, Snowflake and Databricks, discussing their architectures and use cases. Through live coding demonstrations in Python and SQL, you'll learn to work with these technologies hands-on.

Abstract: Introduction to LLM vulnerabilities This introductory course on vulnerabilities for Large Language Models (LLMs) and language models in general. It provides a deep dive into the practical applications of large language models (LLMs) using Azure's AI services. Upon completion, learners will be able to: Explain the concept of model replication or model shadowing as a potential attack vector in large language models, and describe methods to mitigate it through techniques like rate limiting and buffering. Analyze the potential benefits and limitations of using pre-trained LLMs Develop strategies for mitigating risks and ethical considerations when deploying LLM-powered applications. Describe the high-level process of creating a large language model, including data collection, cleaning, and training. Explain the role of security in large language models and recognize potential security vulnerabilities and attack vectors. Identify insecure plugin designs in large language model software development kits (SDKs) that could lead to remote execution and implement strategies to secure plugins. You will learn how to secure your large language model (LLM) applications by addressing potential vulnerabilities. You will explore strategies to mitigate risks from insecure plugin design, including proper input validation and sanitization. Additionally, you will discover techniques to protect against sensitive information disclosure, such as using a redaction service to remove personally identifiable data from prompts and model responses. Finally, you will learn how to actively monitor your application dependencies for security updates and vulnerabilities, ensuring your system remains secure over time. Week 1: Foundations of Language Models This week you will get a brief overview of LLMs and how do they work Learning Objectives Analyze common types of generative applications and their architectures, including multi-model applications, and understand their challenges and benefits. Explain the functioning of a multi-model application, including the role of the framework and specialized machine learning models. Identify the advantages of smaller, specialized models in terms of resource usage, interaction speed, and deployment agility. Compare and contrast different generative AI application types, such as API-based, embedded models, and multi-model applications, and understand their use cases and challenges. Recognize the importance of large language models in various real-world applications, including text-based chatting, customer service, content creation, and daily tasks. Evaluate the benefits and drawbacks of large language models, considering aspects like accuracy, privacy, and potential misuse. Understand the basics of tokenization, indexing, and probability machines in the context of large language models. Describe the high-level process of creating a large language model, including data collection, cleaning, and training. Explain the role of security in large language models and recognize potential security vulnerabilities and attack vectors. Week 2: Language Model Vulnerabilities This week focuses on model-based vulnerabilities that you can explore with prompts. Learning Objectives Explain the concept of model replication or model shadowing as a potential attack vector in large language models, and describe methods to mitigate it through techniques like rate limiting and buffering. Identify and demonstrate insecure output handling in large language models, and understand the potential security threats and attack vectors associated with it. Understand prompt injection and its implications for large language models, including how certain applications define the initial behavior of these models and how to exploit implicit system prompts. Recognize model theft vulnerabilities and understand how handling and access to system components can impact model security, particularly in the context of dynamically loaded models from external sources. Week 3: System vulnerabilities This week you will learn how to deal with environments and system-based vulnerabilities as they relate to LLMs. Learning Objectives Identify insecure plugin designs in large language model software development kits (SDKs) that could lead to remote execution and implement strategies to secure plugins. Explain the potential risks of sensitive information disclosure in large language models and implement measures to redact personal identifiable information using HTTP APIs and regular expressions. Monitor and update dependencies in large language model applications to prevent potential security vulnerabilities and automate the process using tools like GitHub's Dependabot. Evaluate application vulnerabilities based on the programming language and framework, and implement measures to prevent potential security threats. Week 4: Other types of vulnerabilities Learning Objectives Identify potential security threats and vulnerabilities associated with large and small language models. Implement strategies to prevent security situations and guard against making environments more secure. Recognize the concept of excessive agency in large language models and its potential impacts on functionality. Explain the denial of service threat for large language models and describe methods to guard against API misuse.% 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 understand and work with vulnerabilities within language models. Resources Introduction to Generative AI Responsible Generative AI and Local LLMS Practical MLOps book.

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: This video series covers key concepts and technologies for virtualizing and containerizing applications. It introduces the benefits of virtualization, compares virtual machines and containers, and provides a deep dive into working with Docker containers. Through live demonstrations, you'll learn to create and manage virtual machines, pull Docker images, and run containerized applications.

Abstract: This comprehensive course teaches you to leverage the power of cloud computing to develop production-ready machine learning solutions. Starting with the fundamentals of cloud storage, security, and infrastructure as code, you will gain a solid foundation in cloud computing concepts. The course then dives into serverless machine learning, teaching you to build and deploy ML models using popular cloud AutoML platforms like Google AutoML, Amazon SageMaker, Microsoft Azure ML, and CreateML. Through hands-on labs, you'll learn to train custom models with just a few lines of configuration, without needing deep ML expertise. Next, the course explores serverless technologies like AWS Lambda, Google Cloud Functions, and Azure Functions to cost-effectively operationalize your ML models at scale. You'll learn to expose your AutoML models as serverless microservices, automatically scaling to handle any load. The course also covers serverless data processing and ETL to build complete ML pipelines. Finally, you'll learn to apply DevOps best practices to your serverless ML applications using continuous integration and delivery (CI/CD), infrastructure as code, monitoring, and containerization technologies like Docker and Kubernetes. By the end, you'll have the skills to build highly scalable end-to-end machine learning solutions leveraging the latest cloud AutoML and serverless technologies.

Abstract: This video series covers the fundamentals of working with Jupyter Notebooks and popular managed notebook platforms. It introduces the Jupyter Notebook interface, explains how to create and manipulate notebook cells, and demonstrates key features like magic commands. It also explores cloud-based notebook environments including Google Colab, AWS SageMaker, and SageMaker Studio, highlighting their unique capabilities and use cases. Through live coding examples, you'll learn to work effectively with these tools for data science and machine learning projects.

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

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

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

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

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

Abstract: 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: 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: 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: 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: 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: 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: 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: Switch to Rust Switching to Rust from Python (Live Rough Draft Series) This video series covers how to switch to Rust from Python. It is in rough draft format and will be updated as the series progresses. The aim is to get this content out to the community as soon as possible. You can follow along with the code on GitHub at * Rust Tutorial- https://nogibjj.github.io/rust-tutorial/ . * Rust MLOps Template- https://github.com/noahgift/rust-mlops-template Lessons Covered Include: Lesson 1: Introduction to Leveling Up from Python to Rust The lesson is an introduction to a "Switch to Rust" course that covers the use of a live book of Rust lecture notes, installation and setup of Rust on AWS Cloud9, using GitHub Copilot for Rust development, and leveling up from Python to Rust. It also compares the energy efficiency of Python and Rust. 1.0 Introduction to Switch to Rust Course 1.1 Live Book of Rust Lecture Notes Overview 1.2 Rust Installation and Setup on AWS Cloud9 and explain why different than Python 1.3 Using GitHub Copilot to assist in Rust development and Leveling Up from Python. 1.4 Leveling up rust packaging actix microservice and crates 1.5 Energy efficiency of Python vs Rust Lesson 2: Building High Performance Rust Command Line Interfaces for MLOps, Distributed Systems, and Data Engineering This lesson describes the use of Rust for high performance Machine Learning Operations (MLOPs) with Hugging Face, including a comparison of systems programming in Rust and Python. It covers the creation of a high-performance data engineering tool in Rust that deduplicates data using Rayon for safe and memory efficient concurrency. The lesson also discusses building a high-performance GPU-accelerated Hugging Face Translator CLI using Rust. 2.0 High Performance MLOPs with Rust Hugging Face doing Zero Shot Classification 2.1 systems programming rust vs python for MLOPs 2.2 Building a high-performance data engineering tool with Rust that deduplicates data using Rayon with safe and memory efficient concurrency 2.3 Building a high-performance GPU-accelerated Hugging Face Translator CLI with Rust Lesson 3: Building GPU-accelerated MLOps with Rust In this lesson, we discuss the setup of reproducible GitHub Codespaces for GPU workflows using Stable Diffusion with Rust PyTorch bindings. Next we highlight the use of Amazon Firecracker in AWS Lambda as one of the most impressive use cases and go on to explain how to train models in Rust using PyTorch that target the GPU. The lesson also covers the creation of a CUDA-enabled stress test using Rust PyTorch bindings and Rayon for multithreaded GPU-accelerated MLOps 3.0 Reproducible GitHub Codespaces repo setup for GPU workflows with Stable Diffusion with Rust PyTorch bindings 3.1 In this video, I talk about one of the most impressive use cases, amazon #firecracker that powers #aws #lambda. Can you get bigger than this? Then I dive into training models in Rust with #pytorch that target the GPU. 3.2 Building a CUDA-enabled Stress Test with Rust PyTorch bindings and Rayon for multithreaded GPU-accelerated MLOps Lesson 4: Building Rust Containerized Solutions Lesson 5: Building Rust Cloud Computing Solutions for AWS 5.1 Building an Actix Web Server with Rust and Deploying to AWS App Runner Learning Objectives Learn program in Rust using Python as a reference and advanced tools like GitHub Copilot to assist Learning to build solutions with Rust including CLI, Web, and MLOps Learning to build Rust solutions for AWS, GCP, and Azure Learning to build Rust solutions for Kubernetes, Docker, and Serverless Learning to build Rust solutions for Data Engineering, Data Science, and Machine Learning Operations (MLOps) Learning to build Rust solutions for GPU-accelerated Machine Learning Learning the key syntax and features of the Rust Language Additional Popular Resources Pytest Master Class AWS Solutions Architect Professional Course Github Actions and GitOps in One Hour Video Course Jenkins CI/CD and Github in One Hour Video Course AWS Certified Cloud Practitioner Video Course Advanced Testing with Pytest Video Course AWS Solutions Architect Certification In ONE HOUR Python for DevOps Master Class 2022: CI/CD, Github Actions, Containers, and Microservices MLOPs Foundations: Chapter 2 Walkthrough of Practical MLOps Learn Docker containers in One Hour Video Course Introduction to MLOps Walkthrough AZ-900 (Azure Fundamentals) Quick reference guide 52 Weeks of AWS Episode 8: Infrastructure as Code with CDK and AWS Lambda Learn GCP Cloud Functions in One Hour Video Course Python Devops in TWO HOURS! MLOps Platforms From Zero: DatMLOps Platforms From Zero: Databricks, MLFlow/MLRun/SKLearn AWS Machine Learning Certification In ONE HOUR Fast, documented Machine Learning APIs with FastAPI Zero to One: AWS Lambda with SAM and Python in One Hour AWS Storage Solutions 2022: EBS/S3/EFS/Glacier Python Bootcamp for Data Testing In Python book Minimal Python book Practical MLOps book Python for DevOps-Playlist.

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

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: 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: 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: 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: 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: 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 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: 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: 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: 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 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: 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 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: Assimilate GitHub Learning GitHub During Live Coding This video series covers live coding GitHub iteratively thus learning the platform. Lessons Covered Include: 1.0 ep1 getting started with GitHub Codespaces Actions Learning Objectives Learn GitHub Learning to build solutions with GitHub Learning the key technologies of GitHub like Actions, Codespaces, and more Additional Popular Resources Pytest Master Class AWS Solutions Architect Professional Course Github Actions and GitOps in One Hour Video Course Jenkins CI/CD and Github in One Hour Video Course AWS Certified Cloud Practitioner Video Course Advanced Testing with Pytest Video Course AWS Solutions Architect Certification In ONE HOUR Python for DevOps Master Class 2022: CI/CD, Github Actions, Containers, and Microservices MLOPs Foundations: Chapter 2 Walkthrough of Practical MLOps Learn Docker containers in One Hour Video Course Introduction to MLOps Walkthrough AZ-900 (Azure Fundamentals) Quick reference guide 52 Weeks of AWS Episode 8: Infrastructure as Code with CDK and AWS Lambda Learn GCP Cloud Functions in One Hour Video Course Python Devops in TWO HOURS! MLOps Platforms From Zero: DatMLOps Platforms From Zero: Databricks, MLFlow/MLRun/SKLearn AWS Machine Learning Certification In ONE HOUR Fast, documented Machine Learning APIs with FastAPI Zero to One: AWS Lambda with SAM and Python in One Hour AWS Storage Solutions 2022: EBS/S3/EFS/Glacier Python Bootcamp for Data Testing In Python book Minimal Python book Practical MLOps book Python for DevOps-Playlist.

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

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

Abstract: Python for DevOps 2023: GitHub Codespaces CI/CD Microservice Pipeline Deployed to AWS App Runner and GCP Cloud Run Learning The Core Principles of Python for DevOps From Zero During Live Coding This video series covers live coding a step by step Python Microservice that serves out geopy requests that convert to wikipedia pages that get convereted to key phrases via a FastAPI microservice that is deployed to AWS App Runner and GCP Cloud Run. Lessons Covered Include: How to setup a GitHub Codespaces CI/CD Pipeline from Zero How to arrange Python libraries and test them How to containerize a Python Microservice How to setup continuous delivery using AWS Cloud9 and ECR How to integrate AWS Code Build to do a CI/CD Pipeline that deploys to AWS App Runner How to setup continuous delivery using GCP Cloud Shell and GCR Learning Objectives Learn Python for DevOps using live coding Learning to build solutions with Python and FastAPI and Containers. Learning the key syntax and features of the Python Language Additional Popular Resources Pytest Master Class AWS Solutions Architect Professional Course Github Actions and GitOps in One Hour Video Course Jenkins CI/CD and Github in One Hour Video Course AWS Certified Cloud Practitioner Video Course Advanced Testing with Pytest Video Course AWS Solutions Architect Certification In ONE HOUR Python for DevOps Master Class 2022: CI/CD, Github Actions, Containers, and Microservices MLOPs Foundations: Chapter 2 Walkthrough of Practical MLOps Learn Docker containers in One Hour Video Course Introduction to MLOps Walkthrough AZ-900 (Azure Fundamentals) Quick reference guide 52 Weeks of AWS Episode 8: Infrastructure as Code with CDK and AWS Lambda Learn GCP Cloud Functions in One Hour Video Course Python Devops in TWO HOURS! MLOps Platforms From Zero: DatMLOps Platforms From Zero: Databricks, MLFlow/MLRun/SKLearn AWS Machine Learning Certification In ONE HOUR Fast, documented Machine Learning APIs with FastAPI Zero to One: AWS Lambda with SAM and Python in One Hour AWS Storage Solutions 2022: EBS/S3/EFS/Glacier Python Bootcamp for Data Testing In Python book Minimal Python book Practical MLOps book Python for DevOps-Playlist.

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

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

Abstract: Assimilate AWS Cloud Development Kit (CDK) Learning CDK During Live Coding This video series covers live coding CDK iteratively. Lessons Covered Include: 1.0 getting started with AWS Cloud9 and the AWS CDK or AWS Cloud Development Kit Learning Objectives Learn Infrastructure as Code (IaC) with AWS CDK Learning to build solutions with CDK Learning the key syntax and features of the CDK Python API and other languates 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 Containers Learning Containers During Live Coding This video series covers live coding Containers iteratively while learning the concepts. Lessons Covered Include: 1.0 ep1 getting started containers build tiny bash container Learning Objectives Learn Containers Learning to build solutions with Docker, Containers and Kubernetes Learning the key syntax and features of Containers Additional Popular Resources Pytest Master Class AWS Solutions Architect Professional Course Github Actions and GitOps in One Hour Video Course Jenkins CI/CD and Github in One Hour Video Course AWS Certified Cloud Practitioner Video Course Advanced Testing with Pytest Video Course AWS Solutions Architect Certification In ONE HOUR Python for DevOps Master Class 2022: CI/CD, Github Actions, Containers, and Microservices MLOPs Foundations: Chapter 2 Walkthrough of Practical MLOps Learn Docker containers in One Hour Video Course Introduction to MLOps Walkthrough AZ-900 (Azure Fundamentals) Quick reference guide 52 Weeks of AWS Episode 8: Infrastructure as Code with CDK and AWS Lambda Learn GCP Cloud Functions in One Hour Video Course Python Devops in TWO HOURS! MLOps Platforms From Zero: DatMLOps Platforms From Zero: Databricks, MLFlow/MLRun/SKLearn AWS Machine Learning Certification In ONE HOUR Fast, documented Machine Learning APIs with FastAPI Zero to One: AWS Lambda with SAM and Python in One Hour AWS Storage Solutions 2022: EBS/S3/EFS/Glacier Python Bootcamp for Data Testing In Python book Minimal Python book Practical MLOps book Python for DevOps-Playlist.

Abstract: Assimilate Google Cloud Learning During Live Coding This video series covers live coding the Language iteratively thus learning the language. Lessons Covered Include: 1.0 getting started Google Cloud shell apis cloudrun Learning Objectives Learn Google Cloud Learning to build solutions with Google Cloud Learning the key syntax and features of the Python Language on 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: First episode of the series 52 weeks of MLOps. This episode starts with Tensorflow. 00:00 Intro 03:00 Walk through of options for deploying Tensorflow 06:00 Using Colab Notebook runtime options including GPU and TPU 08:00 Creating a copy of Colab into Github 09:41 Using MNIST dataset to do work with Keras 12:48 Training a model with Keras in Colab 14:00 Running a simple TFX Pipeline in Colab with Tensorflow 23:00 Inspecting the contents of the exported Tensorflow model on disk.

Abstract: More Hugging Face exploration of datasets 00:00 Intro 01:08 Trying to figure out what to explore in Hugging Face 02:00 Kicking the tires on Auto-Train 06:00 Finding datasets on Hugging Face 13:00 Creating new dataset repository 17:00 Looking through options for dataset repository hosting.

Abstract: Sagemaker pipelines and the AWS ML Certification 00:00 Intro 03:00 Define business objectives 05:00 Problem framing 07:20 Problem formulation 11:10 What data do you need? 12:37 Observations 14:15 Storing data in AWS 16:21 ETL 17:41 Securing data 19:19 Loading data into pandas 20:18 Descriptive statistics 23:25 Feature selection and extraction 24:40 Encoding ordinal data 25:48 Cleaning data 26:39 Missing values.

Abstract: Live coding Ludwig inside of Github Codespaces 00:00 Intro 02:28 Introduction to Ludwig 04:57 Creating Github Codespaces for Ludwig 08:30 Installing Ludwig inside of Github Codespaces with Ubuntu 12:32 Grabbing data for Ludwig to train Rotten Tomotoes on and do binary classification 16:48 Trying to figure out why lzma is not working 22:50 Failing hard on figuring out why lzma is not working (note need to compile Python). Will fix in next episode..

Abstract: Continue to learn about AWS Solutions Architecture on the topic of Decoupling 00:00 Intro 04:00 Forms of decoupling 07:00 SQS 12:00 SQS Use Cases 19:27 SNS 23:21 SNS vs SQS.

Abstract: 52 Weeks of Apple Swift. This episode covers methods 00:00 Intro 01:01 Instance methods 03:40 The self Property 05:29 Modifying Value Types from Within Instance Methods 05:54 Type methods.

Abstract: Learn about caching content in the next episode of 52 weeks of AWS 00:00 Intro 00:40 Trading capacity for speed 03:00 Why Cache? 06:00 CloudFront 12:00 DDoS mitigation 14:00 Sticky sessions 17:00 Side Caches 20:00 Memcached versus Redis 21:09 Caching Strategies.

Abstract: Learn Swift deinit and deinitializer methods 00:00 Intro 01:59 How deinitialization works 04:29 Building deinit methods 05:43 Running deinit methods.

Abstract: In this episode of 52 weeks of Swift I talk about inheritence in Swift and how it is used to create a more robust toolchain for iOS and macOS. 00:00 Intro 00:50 Property Values 03:00 Invoking a base Class 05:00 Subclassing 06:49 Subclass of a Subclass 08:00 Overriding.

Abstract: Learn 52 weeks episode 7, swift closures 00:00 Intro; 00:36 What is a closure?; 02:00 Sorted; 06:00 Inferring Type From Context; 15:00 Various exotic topics in closures.

Abstract: Learn to connect networks on AWS .

Abstract: Learn to pass the AWS Solutions Architect Exam by learning about Databases 00:00 Intro 03:21 Storage Requirements 07:41 RDS Characteristics 12:03 RDS Demo 16:00 DynamoDB 19:21 DynamoDB Demo 23:40 DynamoDB Demo with AWS CloudShell and Boto3 and Python.

Abstract: 52 Weeks of Apple Swift. This episode covers subscripts. 00:00 Intro 01:43 Subscript Syntax 03:30 Subscript Usage 05:00 Subscript Options 06:47 Subscript Matrix 07:00 Type subscripts.

Abstract: Live coding with Hugging Face models and transformers as well as exploring TFHub 00:00 Intro 01:56 Looking at Tensorflow Hub 04:18 Exploring Tensorflow Hub 05:04 Looking at Pegasus Models in Hugging Face 05:28 Exploring Inference API for Hugging Face Models 07:17 Exploring DistilGPT2 Model in Hugging Face 09:00 Installing Hugging Face in Google Colab notebook 10:49 Using Hugging Face Transformers 12:00 Installing packages in Github CodeSpaces using virtualenv and requirements.txt 13:33 Using Transformers in Hugging Face 15:51 Doing Sentiment Analysis with Hugging Face 20:00 Doing Zero Shot Classification with Hugging Face 22:00 Analyzing Elon Musk statements and FSD (autonomous vehicles) using Zero Shot Classification 26:00 Doing Text Generation with Hugging Face.

Abstract: Episode three covers serving models with TensorFlow Serving 00:00 Intro 02:02 Launching Github CodeSpaces 04:00 Running make install 05:00 Creating Bash script to run tensorflow serving 09:00 Feeding model to Tensorflow Serving 14:00 Querying Tensorflow Serving API 16:00 Trying Docker version of Tensoflow Serving 22:00 Trying MNIST with Tensorflow Serving 23:00 Using Docker pull to speed up the process 27:30 Invoking Tensorflow Serving model endpoint.

Abstract: Continue learning about Solutions Architect certification this time with automation. Opsworks, Beanstalk, Chef and Cloud Formation. 00:00 Intro.

Abstract: In this episode of 52 weeks of AWS I continue to cover the solutions architect certification. The topic of building Microservices is the focus today. 00:00 Intro 00:38 Discuss new weekly office hours from noahgift.com for O'Reilly books and Coursera courses. 02:00 What are Microservices? 03:35 What are Microservices? 04:00 Characteristics of Microservices 06:00 What is a container? 09:00 ECS orchestration 11:00 Decomposing monolithic applications 12:32 AWS Fargate 13:00 What is serverless? 15:00 AWS Serverless offerings 15:30 AWS Lambda 23:00 AWS API Gateway 26:00 AWS Step Functions

Abstract: Episode 22 covers the preparing for Disasters. 00:00 Intro 02:37 Planning for failures 03:34 Avoiding and planning for disasters 05:37 Using the Well-Architected Framework design principles 05:51 Recovery Point objectives (RPO) 06:34 Recovery time objective (RTO) 07:12 Plan for disaster recovery 08:03 Storage and backup building blocks 09:50 S3 Cross-Region replication 10:41 EBS volume snapshots 11:41 File system replication 12:50 Compute Capacity recovery 13:44 Strategies for disaster recovery 15:01 Networking design for resilience 15:34 Databases and recovery 16:45 Automation Services: CloudFormation, Elastic Beanstalk and AWS OpsWorks 17:38 Four disaster recovery strategies: Backup and restore, Pilot light, Warm Standby and Multi-site 18:23 AWS Storage Gateway 24:00 Summary of common DR patterns.

Abstract: I talk with the authors of the new O'Reilly book GPT-3 about a range of topics including why they wrote the book. 00:00 Intro 01:30 Why did you write the book? 11:30 Can you write code by talking with GPT-3? 16:00 Is Async Work + GPT-3 a good combination? 19:30 Replica 23:00 How could someone with no technical background write code by talking with GPT-3? 29:30 Could we have organic technology where companies don't harm humans at scale? 39:00 The challenges of making ethical AI software 42:00 Do Tech companies pretend they cannot be ethical, but don't because it lowers profits? 45:00 You cannot predict what they models will do 49:00 Do tech leaders set the values of their companies? 52:00 Are humans often lacking in moral values with technology as well? 54:00 How you can buy the book and follow the authors.

Abstract: Learn control flow with Swift 00:00 Intro 01:57 For Loops 03:54 For Loops Dictionaries 08:00 While Loops 11:00 Case Statements.

Abstract: Detailed conversation about Declarative AutoML with Piero Molino author of Ludwig and co-founder Predibase. 00:00 Intro 00:50 First meeting Piero at Strata in Moscone Center 10:50 Why Declarative AutoML? 12:00 Introducing Declarative ML Systems 16:44 Ludwig: declarative ML systems on PyTorch 18:06 Github Statistics for Ludwig 19:51 Ludwig training example via CLI 21:09 pip install ludwig and using the Programmatic API 26:32 How does Ludwig work? 26:53 Training with Ludwig 27:34 Predicting with Ludwig 29:59 Running Experiments with Ludwig 31:04 ludwig experiment --dataset 32:09 Input - Encoder - Decoder - Output 34:15 ParallelCNN encoding 35:42 Pretrained Transformers: bert distilbert t5 roberta gpt-2 40:44 concat combiner 41:18 Number features decoding 41:37 Vector featuers decoding 41:52 Sequence features decoding 42:30 Training parameters: batch_size, epochs, learning_rate, ... 43:07 Preprocessing parameters 43:33 Speaker Verification 44:03 Expected Time of Delivery 44:35 Summarization 45:04 Distributed Training, Ludwig on Ray 45:23 Running Ludwig on Ray 47:18 Ludwig Hyperpot with RayTune (Advanced) 48:51 Ludwig on Kubernetes 49:15 Managed Ludwig in Predibase 49:54 Predibase: Low-code ML, High-Performance.

Abstract: Continuing to go through Swift language guide 00:00 Intro 01:46 Person Class 04:03 If else statements 06:34 subscript with getter and setter and function 08:18 putting optional chaining together.

Abstract: Learn to pass the AWS ML Certification with a deep dive into Part 2 of the Sagemaker Pipelines material. 00:00 Intro 02:39 Drop missing values 04:46 Outliers 06:57 Feature selection 10:55 File formats for machine learning 13:46 K-fold cross validation 15:55 Training models with Amazong SageMaker 17:33 XGBoost 19:29 Is your model ready to deploy? 22:05 Creating an endpoint 23:26 Machine learning pipeline 25:30 Confusion matrix 27:27 Sensitivity 29:02 Classification report: ROC graph 30:51 Hyperparameter categories 32:06 Sagemaker hyperparameter tuning.

Abstract: Error Handing with Swift 00:00 Intro 01:07 Propogating Errors 01:47 Looking at an entire error propogation class 03:35 Using guards in Swift 06:01 Using catch in Swift 08:07 Using functions in Swift 09:45 Using a defer.

Abstract: Explore Open AI using Github Codespaces 00:00 Intro 03:12 Getting started with OpenAI 06:45 Using Open AI Quickstart Guide with NLP prompts 09:16 Tokens and probabilities 10:46 Trying out Python Flask with Open AI 12:00 Setting up Github CodeSpaces 14:00 Create and source virtualenv 16:00 Create OpenAI API keys in Visual Studio Code 18:55 Setup requirements.txt for Open AI and Flask 20:00 Run Flask OpenAI NLP web app in Github CodeSpaces with Browser preview 22:00 Understand the OpenAI function and test it out with IPython locally.

Abstract: Learn networking for the Solutions Architect Exam 00:00 Intro.

Abstract: In this episode of 52 weeks of Live Coding MLOps I dive into serving out TensorFlow models to the world. 00:00 Intro 02:00 TensorFlow Serving - What is it? 07:00 TensorFlow Serving colab notebook 08:54 Porting code from Colab to Github 10:00 Build Python Tensorflow model scaffolding 13:23 Using pip freeze to get a list of dependencies 17:00 Attemping a first script for MLOps 20:00 Saving the TensorFlow model 24:00 Installing debian packages for TensorFlow serving 29:00 Trying and failing to invoke the model.

Abstract: Learn to prepare for the AWS Solutions Architect Exam.

Abstract: Diving into C# and .NET from the beginning with Live coding each week. 00:00 Intro 02:38 New Repository 05:04 Launch Github Codespaces 11:30 Trying out .NET Blazor 16:13 Editing Blazor Pages 22:53 Setting up Github Actions to build and test C#.

Abstract: More Hugging Face exploration by Building a Computer Vision Predictor with Gradio 00:00 Intro 00:42 Hugging Face walkthrough 02:09 Create new Hugging Face Space 03:28 Build Gradio app in Hugging Face browser 04:44 Experiment with Gradio UI 07:31 Try Hosted Inference API 09:28 Try DALL-E 17:00 Predict cats with computer vision model 22:00 Try to load packages inside of Spaces user interface browser only 27:00 Toggling to Github CodeSpaces 32:59 Getting Gradio Computer Vision URL predictor working in Github CodeSpaces.

Abstract: Exploring concurrency options with Swift 00:00 Intro 01:14 Asynchronous code 03:20 Looking at await 06:19 Using async functions 07:00 Looking at for try await 08:59 Looking at async let 10:53 Exploring actor.

Abstract: Continue learning about Solutions Architect certification with the topic of Elasticity. 00:00 Intro 02:00 Reactive architectures 04:39 What is elasticity? 06:00 Scaling horizontal vs vertical 12:35 DynamoDB scaling 15:00 Highly available systems 17:00 Route53 19:16 Monitoring.

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

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

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

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

Abstract: Learn Key Skills in Software Engineering Craftmanship Course An Ongoing Course This video will show you how to use CUDA GPUs with Python. Lessons Covered Include: Introduction to course What is a Makefile and how do you use it? Popular Linux commands Configuring ~/.bashrc Using grep, cut, sort and uniq Introducing GitHub Codespaces Compiling Python in Github CodeSpaces What is DevOps and how do you use it? Kaizen Methodology Learn Continuous Integration Learn key software engineering principles and best practices for building software. Learn both concepts and hands-on techniques for building software. Learn software craftsmanship. Additional Popular Resources Pytest Master Class AWS Solutions Architect Professional Course Github Actions and GitOps in One Hour Video Course Jenkins CI/CD and Github in One Hour Video Course AWS Certified Cloud Practitioner Video Course Advanced Testing with Pytest Video Course AWS Solutions Architect Certification In ONE HOUR Python for DevOps Master Class 2022: CI/CD, Github Actions, Containers, and Microservices MLOPs Foundations: Chapter 2 Walkthrough of Practical MLOps Learn Docker containers in One Hour Video Course Introduction to MLOps Walkthrough AZ-900 (Azure Fundamentals) Quick reference guide 52 Weeks of AWS Episode 8: Infrastructure as Code with CDK and AWS Lambda Learn GCP Cloud Functions in One Hour Video Course Python Devops in TWO HOURS! MLOps Platforms From Zero: DatMLOps Platforms From Zero: Databricks, MLFlow/MLRun/SKLearn AWS Machine Learning Certification In ONE HOUR Fast, documented Machine Learning APIs with FastAPI Zero to One: AWS Lambda with SAM and Python in One Hour AWS Storage Solutions 2022: EBS/S3/EFS/Glacier Python Bootcamp for Data Testing In Python book Minimal Python book Practical MLOps book Python for DevOps-Playlist.

Abstract: Microservices Key Concepts - An Ongoing Series Select Microservice Key Concepts This video series covers key concepts of microservices. Lessons Covered Include: 1.0 benefits microservices intro 1.1 benefits microservice 1.2 Operationalizing Microservices 1.3 ci for microservices 1.4 aws api app runner 1.5 flask random fruit 2.0 containers microservice intro 2.1 containerized dotnet6 2.2 containerized ml Learning Objectives Learn key terminology and concepts in Microservices Understand foundational concepts in Microservices 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.