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

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

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

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

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

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

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

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

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

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

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

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

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

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

Abstract: Tackling methane in the coal sector is a major opportunity for climate action that can also strengthen energy security. Experience shows that there are several steps countries can take today – using existing technologies and tools – that can lead to significant reductions in methane emissions from coal mining. This report highlights the lessons learned in different coal-producing jurisdictions to support the development of smart and effective methane regulation. It then provides detailed guidance on the process of designing, drafting and implementing new regulations. Finally, it discusses the different regulatory approaches currently in use for methane, with the aim of providing a comprehensive toolkit for policy makers.

Abstract: India, the Lifestyle for Environment (LiFE) initiative aims to encourage the adoption of sustainable lifestyles in India and internationally to tackle the challenges of environmental degradation and climate change. This report examines how India has integrated several policies in its energy transition strategy that are aligned with the LiFE initiative, highlighting the potential for behavioural change and consumption choices to help advance energy transitions globally. It then analyses the impact on energy consumption, costs and emissions of measures like those proposed by the LiFE initiative, such as buying an EV or taking public transport, if they were adopted globally. Finally, it considers how India’s first G20 Presidency could strengthen the LiFE initiative by anchoring it in the G20’s current framing of energy transitions and initiating processes to gather experience and best practices of policies and programmes that G20 members are already conducting.

Abstract: Renewables are growing rapidly in the electricity systems around the world as countries seek to improve their energy security, meet emission reduction targets and take advantage of cheaper electricity sources. Thanks to successful use of flexibility resources – from stronger grids and interconnections to demand-side measures, affordable storage and dispatchable power supply – many countries have already securely and efficiently integrated significant shares of variable renewables (VRE) in their electricity generation. As wind and solar continue to grow as a proportion of generation, system level surpluses and periods of lower generation will eventually expand beyond hour-to-hour or daily variations to seasonal timescales. Addressing seasonal variability of renewables means that flexibility resources will be needed to varying extents throughout the year, even on a week-to-week or month-to-month basis.

Abstract: The implementation phase for achieving a net zero steel industry will require robust methodologies for measuring emissions at the site- and product-level, together with data collection frameworks to facilitate comparison and track progress. An existing array of methodologies and frameworks for the steel industry provide a good starting point for efforts to achieve these outcomes, but much work remains to achieve interoperability, transparency and fitness for purpose for net zero. Following an evaluation of these existing methodologies and frameworks, this report provides “net zero principles” to guide potential next steps for their development and implementation, together with specific recommendations for G7 members. In the context of Japan’s G7 Presidency, the Ministry of Economy, Trade and Industry requested the International Energy Agency (IEA) to examine the topic of Emissions Measurement and Data Collection for a Net Zero Steel Industry. This work complements that undertaken during Germany’s G7 Presidency in 2022 – Achieving Net Zero Heavy Industry Sectors in G7 Members – providing insights and direction for the G7 Industrial Decarbonisation Agenda with regard to tackling industrial emissions.

Abstract: Since the last review in 2016, Italy has raised its climate ambitions by aiming for carbon neutrality by 2050, and the country is on track to reach its 2030 targets for emissions reductions and energy efficiency. The government has taken encouraging initial steps to overcome the long permitting procedures, administrative burdens and increasing local opposition that have delayed new renewable installations. Italy in 2022 successfully reduced its reliance on Russian natural gas imports, by signing new contracts with alternative suppliers, making use of the pipeline and LNG infrastructure that it has built up over the last decade. Reducing overall demand for natural gas through an accelerated shift to alternative energy sources and a stronger focus on energy efficiency, especially in the building sector, will not only further strengthen energy security, but also help the country meet its climate targets. In this report, the IEA provides energy policy recommendations to help Italy effectively transform its energy sector in line with its goals.

Abstract: The oil and gas industry has some of the best and most cost-effective opportunities to reduce methane emissions. The potential to do so is clear. Some countries and companies have already demonstrated that achieving near-zero emissions from oil and gas operations is technically and economically possible. There are a growing number of initiatives, policies and regulations aiming to reduce emissions globally, and many reductions can be realised while saving money. However, overall progress has been much too slow, despite the record profits that the oil and gas industry saw in 2022. This report looks in detail at the investment requirements to deliver a sharp reduction in oil and gas methane emissions to 2030, and how these could be financed. The analysis is intended to inform discussions in the runup to COP28 and help prompt the necessary actions to accelerate the pace of change.

Abstract: This table on air and climate contains indicators for carbon dioxide (CO2) emissions generated from fossil fuel combustion in IEA countries and regions. Data are available from 1960 onwards.

Abstract: In this table, calorific values for different types of coal and coal products are shown on a country by country basis. The aggregates for hard coal and brown coal are included for years prior to 1978 only. This table provides the calorific values used to convert raw tonnes of coal and coal products into energy for the OECD Coal Balance data. It is shown in megajoules per tonne for OECD countries.

Abstract: This dataset includes statistics on energy technology Research & Development and dissemination as well as R&D budget for IEA countries. It presents shifts in R&D, expenditures associated with investments and further analyzes budget allocations in terms of flow.

Abstract: This table includes the average conversion factors from tonnes to barrels, for the OECD countries and for 24 products.

Abstract: This table includes monthly OECD net electricity production from main sources and trade data from January 2000 to December 2010.

Abstract: This dataset provides statistics on electricity exports by destination country.

Abstract: This table presents detailed coal import data by origin for OECD member states for the following coal products and aggregates: anthracite, coking coal, subbituminous coal, ignite/brown coal, coke, oven coke, hard coal, brown coal, steam coal, other bituminous coal and anthracite. The data are reported in kilotonnes.

Abstract: This dataset contains data on oil demand and consumption by product such as aviation fuels, motor gasoline, diesel oil, kerosene, naphtha, petroleum and refinery fuel for non-OECD countries

Abstract: A massive scaling up of investment is essential in emerging and developing economies to sustainably meet rising demand for energy, as well as to ensure that climate targets are met. Getting on track for net zero emissions by 2050 will require clean energy spending in emerging and developing economies to more than triple by 2030 – far beyond the capacity of public financing alone and therefore demanding an unprecedented mobilization of private capital. This special report by the International Energy Agency (IEA) and International Finance Corporation (IFC) examines how to scale up private finance for clean energy transitions by quantifying the investments required in different regions and sectors to build modern, clean energy systems, including achieving universal access. The new global energy economy represents a huge opportunity for growth and employment in emerging and developing economies. This report's analysis identifies key barriers and how to remove them – and sets out the policy actions and financial instruments that can deliver a major acceleration in private capital flows for the energy transition.

Abstract: In this table, a balance for different types of coal and coal products, including manufactured gases is shown. The aggregates for hard coal and brown coal are also included, as is a product coal which includes all primary coal types, and all derived coal products with the exception of gas works gas. This table provides full balance data such as primary energy supply, transformation sector and final consumption based in kilotonnes of coal equivalent (7,000 Gigacalories) for OECD countries and OECD country aggregate groups.

Abstract: This table contains data on natural gas imports by origin for OECD countries.

Abstract: This dataset provides wholesale and retail energy end-use price indices by sector for both household and industry in nominal and real terms. Products such as coal, electricity, natural gas and oil products are included within the table.

Abstract: Today, oil and gas operations account for around 15% of total energy-related emissions globally, the equivalent of 5.1 billion tonnes of greenhouse gas emissions. In the International Energy Agency’s Net Zero Emissions by 2050 Scenario, the emissions intensity of these activities falls by 50% by the end of the decade. Combined with the reductions in oil and gas consumption in this scenario, this results in a 60% reduction in emissions from oil and gas operations to 2030. Fortunately, oil and gas producers have a clear opportunity to address the problem of emissions from their activities through a series of ready-to-implement and cost-effective measures. These include tackling methane emissions, eliminating all non-emergency flaring, electrifying upstream facilities with low-emissions electricity, equipping oil and gas processes with carbon capture, utilisation and storage technologies, and expanding the use of hydrogen from low-emissions electrolysis in refineries. Upfront investments totalling USD 600 billion would be required to halve the emissions intensity of oil and gas operations globally by 2030. This is only a fraction of the record windfall income that oil and gas producers accrued in 2022 – a year of soaring energy prices amid a global energy crisis. This report aims to inform discussions on these issues in the run-up to the COP28 Climate Change Conference in Dubai in November and is part of a broader World Energy Outlook special report to be released later in 2023 focusing on the role of the oil and gas industry in net zero transitions.

Abstract: This report was commissioned by Korea's Ministry of Trade, Industry and Energy and carried out jointly by the International Energy Agency (IEA) and the Korea Energy Economics Institute. The objective of the study was to provide high-level policy recommendations on Korea’s clean energy transition from coal in the power sector. The report covers a detailed review of policy and market developments around Korea’s transition to net zero. The scope of the report includes all sectors of the economy, industry and all the regions across the world, where we extract the main recommendations that are applicable to the case of Korea. Currently, the power sector is the largest CO2-emitting sector and coal is the single biggest source of CO2 emissions, as it is the backbone of many electricity systems. Thus, coal power plants have been a target for reaching net zero emissions by 2050 for long time. Korea has firm objectives to achieve carbon neutrality by 2050, aiming to accelerate the clean energy transition of coal power plants. Policy recommendations were formulated around two priorities: affordable and secure supply of electricity and people-centred transition.

Abstract: The International Energy Agency (IEA) and the Institute of Energy Economic, Japan (IEEJ) have developed and published long-term decarbonisation pathways for Southeast Asia and Indonesia. This report provides a comparison of modelling approaches, quantitative drivers, and results from the IEA and IEEJ pathways, highlighting areas of agreement, as well as identifying and explaining differences, and thereby to derive implications.

Abstract: Power markets are an instrument used globally to ensure electricity security while maintaining affordability and incentivising decarbonisation. The People’s Republic of China (hereafter, “China”) has been making big steps towards implementing markets, and the goals announced in 2020 of carbon dioxide emissions peaking before 2030 and carbon neutrality before 2060 have added momentum to expand their footprint. Provinces have taken a leading role in designing and implementing markets. To improve sharing of resources, the guidance to implement a unified national market system pushes for more co-ordination between provinces. This report examines the role of power markets in China and the pathways to develop a national market. The analysis focuses on short-term markets because they have the potential to unlock flexibility the system needs in light of renewables growth and changing weather patterns. The report provides recommendations to improve markets’ co-ordination across the country as well as within the provinces.

Abstract: This dataset on energy prices provides statistics on the crude oil import costs both in terms of USD/bbl or real index per national currency.

Abstract: This table presents detailed coal export data by destination for OECD member states for the following coal products and aggregates: anthracite, coking coal, sub-bituminous coal, lignite/brown coal, coke oven, coke, hard coal, brown coal, steam coal, other bituminous coal and anthracite. The data are reported in kilotonnes.

Abstract: This dataset provides statistics on electricity supply per OECD country with net electricity production from main sources and trade data from January 2000 onwards. Data are presented on a monthly basis.

Abstract: A future with net zero emissions requires scaling up improvements in energy efficiency, electrification of end uses and renewable energy generation. For the Association of Southeast Asian Nations (ASEAN), a growing population and rising standards of living will massively increase future energy demand. Mitigating growing electricity demand and integrating renewable energy into electricity generation will therefore be paramount for the region's clean energy transition and avoid lock-in of additional fossil fuel generation. The intermittent nature of variable renewable energy and increasing deployment of distributed energy resources are putting additional pressure on existing grids. In response to these challenges, this report explores the opportunities and challenges for efficient grid-interactive buildings in the ASEAN region. Such buildings are becoming a crucial element for the global ambition to attain net zero emissions, as they can combine enhanced energy efficiency, advanced smart digital technologies and decarbonised electricity generation. This integration creates the potential for buildings to shift from energy-intensive consumers to low-carbon prosumers, empowered by digital technologies that can offer flexibility benefits to the electricity grids. With the ability to produce, consume, store, sell and buy energy, buildings become active participants in the building-to-grid ecosystem. Drawing on relevant international trends and best practices, the current report lays out an analytical framework to assess a variety of factors that can enable a building to become energy efficient and grid-interactive. The framework is used to analyse the current situation in countries of the ASEAN region. The report provides policy-oriented recommendations and guidelines tailored to different stages of the process for adopting efficient grid-interactive solutions in buildings. These recommendations can support ASEAN policy makers in their policy development to create an energy-efficient and grid-interactive built environment, contributing to a cleaner and more sustainable energy future.

Abstract: Technology manufacturing plays a pivotal role in the energy transition required to meet climate, energy security and economic development goals. Deploying clean energy technologies at the pace required to put the world on a trajectory consistent with net zero emissions by mid-century will demand rapid expansion in manufacturing capacity, underpinned by secure, resilient and sustainable supply chains for their components and materials. The State of Clean Technology Manufacturing: Energy Technology Perspectives Special Briefing provides an update on recent progress in clean energy technology manufacturing in key regions. It focuses on five technologies – solar PV, wind, batteries, electrolysers and heat pumps – that will be critical to the energy transition. Manufacturing capacity for these technologies is expanding rapidly, driven by supportive policies, ambitious corporate strategies and consumer demand. The aim is to keep decision makers informed of investment trends and the impact that recent industrial strategies are having in these highly dynamic sectors. This special briefing was produced to support deliberations at the 2023 G7 Leaders' Summit in Hiroshima, Japan, from 19-21 May 2023. It builds on analysis in the latest edition of the IEA's flagship technology publication, Energy Technology Perspectives 2023 (ETP-2023), published in January 2023, to take into account the latest announced expansions in manufacturing capacity.

Abstract: In this table, statistics for different types of coal and coal products, including manufactured gases is shown.This table provides full supply and consumption data of coal such as primary energy supply,transformation sector, energy sector and final consumption based in kilotonnes for OECD countries and OECD country aggregate groups.

Abstract: This table provides a reduced electricity and heat balance (145 countries + 22 aggregates; 8 flows).

Abstract: This dataset provides data on energy world indicators in terms of world supply, consumption and trade.

Abstract: This dataset covers 30 countries and 1 indicator.

Abstract: This table contains data on world oil statistics while focusing on trade flows amongst countries, oil demand and output. Data are expressed in kilotonnes or kilobarrels per day.

Abstract: This dataset figures reprensenting various renewable and waste energy supply statistics while showcasing the renewable energy market's products as well as figures for those products in terms of electricity output (GWh, heat output(TJ), domestic supply and stock changes.

Abstract: This table focuses on the import of various energy products such as crude oil, refinery gas, natural gas liquids, fuel oil amongst OECD member countries.

Abstract: The Global Hydrogen Review is an annual publication by the International Energy Agency that tracks hydrogen production and demand worldwide, as well as progress in critical areas such as infrastructure development, trade, policy, regulation, investments and innovation. The report is an output of the Clean Energy Ministerial Hydrogen Initiative and is intended to inform energy sector stakeholders on the status and future prospects of hydrogen, while also informing discussions at the Hydrogen Energy Ministerial Meeting organised by Japan. Focusing on hydrogen's potentially major role in meeting international energy and climate goals, the Review aims to help decision makers fine-tune strategies to attract investment and facilitate deployment of hydrogen technologies at the same time as creating demand for hydrogen and hydrogen-based fuels. It compares real-world developments with the stated ambitions of government and industry. This year's report includes a focus on demand creation for low-emission hydrogen. Global hydrogen use is increasing, but demand remains so far concentrated in traditional uses in refining and the chemical industry and mostly met by hydrogen produced from unabated fossil fuels. To meet climate ambitions, there is an urgent need to switch hydrogen use in existing applications to low-emission hydrogen and to expand use to new applications in heavy industry or long-distance transport.

Abstract: This report assesses the impact of the road transport sector on energy demand, CO2 emissions and air pollution in several selected major emerging economies over the coming decades under several IEA modelling scenarios. Most notably the Announced Pledges Scenario (APS) aims to show to what extent announced ambitions and targets, including the most recent ones, are on the path to deliver emissions reductions required to achieve net zero emissions by 2050. Bringing about a road transport decarbonisation pathway in line with the APS in the selected major emerging economies - Brazil, People's Republic of China, India, Indonesia, Mexico and South Africa - will require significant enhancement of existing policies and the introduction of new innovative policies and measures in each of selected countries. Our report sets out six policy areas critical to the achievement of the road transport transitions and a series of recommendations for strengthening financing for the sector. Importantly, the report provides detailed reference to a wide range of policy measures and good practice already in place in many major emerging economies elsewhere to facilitate knowledge sharing among countries. It also places a special emphasis on the road transport sectors of India and Indonesia. These countries are IEA partners in their respective regions and benefit from an enhanced programme of work.

Abstract: This dataset provides conversion factors in terms of trade, production and consumption of energy within various industries.

Abstract: This table contains data on crude supply by comparing imports, exports, stock changes and trade flows of products such as crude oil, natural gas liquid, biofuels, hydrocarbons and refinery feedstocks.

Abstract: This dataset provides crude oil imports costs by type of crude in terms of country of origin.

Abstract: This database comprises various energy product prices expressed in national currency per toe with products such as sulphur fuel oil, automotive diesel, premium leaded gasoline while comparing data by sectors.

Abstract: This dataset provides wholesale and retail energy end-use price indices by sector for both household and industry in nominal and real terms. Products such as coal, heavy fuel oil, automotive diesel fuel, natural gas, oil products, unleaded gasoline are the core focus of this dataset.

Abstract: This dataset includes oil product spot prices by market. Products such as gasoil, gasoline, high sulphur fuel oi, jet kerosene, naphtha are listed within the database.

Abstract: This database comprises various energy product prices expressed in US dollars with products such as sulphur fuel oil, automotive diesel, premium leaded gasoline while comparing data by sectors.

Abstract: The world remains far off track to reach universal access to electricity by 2030 – a key target canonised in UN Sustainable Development Goal 7 (SDG7). While many countries have set targets to advance universal electrification, it remains challenging to collect timely data on their progress, making it difficult to build or refine their electrification plans and policy strategies. To provide governments with the tools to achieve their goals, the Guidebook for Improved Electricity Access Statistics (hereafter “Guidebook”) focuses on methodologies using readily available supply-side data from electric utilities, mini-grid operators, and off-grid system distributors to track access to electricity trends. This approach can be adopted at a low-cost and provides accurate estimates on access rates with a potential time lag on the order of months, instead of years. Supply-side data complements household or census surveys, which can give a more nuanced, detailed picture, but are typically only run every five to ten years due to their expense. This report provides step-by-step guidelines on how to implement an improved supply-side data collection process, and produce more time-sensitive and comprehensive access to electricity indicators. In addition, the Guidebook proposes a standard method to capture mini-grids and stand-alone off-grid systems in calculating access to electricity, which are becoming increasingly more commonplace in strategies to reach universal access. Finally, the Guidebook suggests further steps practitioners can take to advance geospatial data collection and reporting, which has become the new best practice for electrification planning supporting both electric utilities and the vibrant off-grid industry.

Abstract: The Clean Energy Transitions Programme 2022 (CETP) provides an overview of the programme’s major achievements in the last year. During this time, the CETP has driven forward an ambitious and impactful agenda, even as the world has contended with multiple overlapping crises. As just some examples, the CETP delivered the Energy Sector Roadmap to Net Zero Emissions in Indonesia , which ministers strongly welcomed at the G20, and has played a key role in shaping Indonesia’s landmark Just Energy Transition Partnership (JETP). Similarly, the IEA has been able to take its engagement to a whole new level with various African countries, such as with its first-ever report on the Clean Energy Transitions in the Greater Horn of Africa , and provided policy advice that was directly accepted into China’s latest five-year plan. More broadly, it has also supported strategically important work on critical minerals, energy employment and clean energy investment. Overall, the CETP’s combination of data, insight, advice and capacity building is making a strong positive contribution to advancing clean energy transitions around the world – with a focus on emerging and developing economies. In the five years since its launch, the CETP has become the largest and most important programme at theInternational Energy Agency (IEA). It has been the main vehicle through which theIEA has established, deepened and strengthened partnerships with major nonmember countries – those countries that are at the forefront of the clean energytransition. The programme has also played a crucial role in enabling the IEA tostrengthen its global leadership role in clean energy transitions at a pace and scalethat would otherwise not have been possible, with great benefit to theprogramme’s focus countries and IEA member countries alike.

Abstract: Countries around the world are facing an energy security and affordability crisis affecting households, businesses and public budgets. In Latin America, household expenditure on energy services is second only to food. On average, the share of household budget for energy ranges between 7% and 9% of income – and up to more than 24% for the poorest households. As governments urgently implement measures to protect citizens and secure their economies, it is important to consider short- and long- term solutions to ease existing pain and reduce risks over time. While much attention focuses on securing energy supplies, it is equally important to take a hard look at the role of more efficient and flexible demand in securing affordable, reliable energy.Decades of policies and programmes have delivered energy savings around the world. As a result, energy systems are more resilient, bills are lower, and public budgets feel less pressure than they would have without these efforts. This report looks at energy efficiency and demand management policies and programmes across Latin America. It presents evidence of the results of these efforts on improving living standards, public budgets, energy security and employment, and provides a view on the opportunities to expand these benefits and support just energy transitions.

Abstract: Energy efficiency indicators are key to tracking energy efficiency progress for a variety of purposes (e.g. policy making, monitoring targets, making energy projections, developing scenarios and planning, and benchmarking). This guide is for professionals and decision makers, describing options and good practices for the collection of energy end-use data and the development of energy efficiency indicators at the national level. In parallel, it can also be used as an assessment tool, helping countries/economies to locate their starting point, and to identify appropriate targets according to their respective national interests and priorities.The roadmap presented here encompasses the results of a consultation exercise across countries and presents good practices and practical tips. It acknowledges that there is no single solution, but a number of possible pathways instead, depending on national contexts and priorities. The roadmap is a strategic document looking at the whole value chain in the development of efficiency indicators, from the initial point where the need for data and indicators arises up to the later dissemination and data use stages, and is meant to be a useful resource for practitioners across the globe in the development of energy efficiency indicators.

Abstract: This table focuses on the export of various energy products such as crude oil, refinery gas, natural gas liquids, fuel oil amongst OECD member countries.

Abstract: The clean energy transition requires a fundamental transformation of power systems, including much higher levels of digitalisation at scale across all grid domains, from generation to transmission and distribution to end-use. Strong policy attention is required to scale up investments in smarter and more resilient grids in emerging and developing economies where electricity consumption is set to grow at a rapid rate while also providing greater levels of electricity access. Investments in smarter and more resilient grids will be necessary to accommodate the greater deployment of renewable energy and enhance energy security. Digital technologies designed for power systems are instrumental to unlock essential system services required to integrate high shares of variable renewable energy. They can also provide solutions to leverage data flows, connectivity, and management across the whole electricity system. To unlock these digital opportunities, adequate planning, investment, and policy action are needed. As part of the Digital Demand Driven Electricity Networks (3DEN) initiative, this report provides guidance for energy policy makers on possible ways to enable and drive investments in smart and resilient electricity grids. It also gives suggestions on how to start creating an environment that supports the effective use of innovative digital technologies within the electricity sector. It draws on examples and case studies to show the wide range of digital opportunities and solutions that can help governments implement efficient and smart power systems.

Abstract: This table contains data on natural gas exports by origin for OECD countries.

Abstract: This dataset comprises statistics regarding various renewable and waste energy while presenting trade statistics of various renewable energy products as well as figures for those products in terms of electricity output (GWh) and heat output(TJ).

Abstract: This table contains world data on supply of natural gas.

Abstract: This dataset brings together statistics compiled by the IEA on natural gas imports by origin. It contains time series for 1993 onwards for the world, 143 countries and 24 regional aggregates on imports of natural gas by country of origin.

Abstract: This comparative tables presents figures on the renewable energy supply and consumption market amongst OECD member countries by analyzing renewable products in terms of production, import and exports.

Abstract: 31 countries (30 individual countries + European Union), 8 products and 11 flows (8 summary groups of energy technologies + Total + Memos: Low-carbon and Non-low-carbon)

Abstract: This table contains world data on natural gas supply as well as natural gas consumption.

Abstract: This comparative table comprises statistics about net capacity of renewables in MWe terms. Renewable electricity and heat generation are derived from solar thermal, geothermal, hydro, wind.

Abstract: This table shows a comprehensive status of net electrical capacity by type of energy. It also contains data on single fuel-fired and multi-fuel fired plants, type of generation and peak load.

Abstract: This dataset focuses on the renewables market while providing full balance data such as primary energy supply, transformation sector and final consumption based in kilotonne and terajoule of renewables equivalent for OECD countries and OECD country aggregate.

Abstract: This dataset includes various crude oil products listed by their respective market spot prices.

Abstract: This dataset includes energy statistics on the basis of main economic indicators such as Consumer Price Index, Purchasing Power Parities, Producer Price Index and currency exchange rates.

Abstract: This database comprises various energy product prices expressed in national currency per unit with products such as natural gas, steam coal, coking coal, liquefied petroleum gas while comparing data by sectors.

Abstract: Finland has set one of the most ambitious climate targets in the world, a legal obligation to reach carbon neutrality by 2035. It has made notable progress towards this target, deploying the first new nuclear reactor in Europe in over 15 years and strongly expanding wind generation. Thanks to the progress Finland has made on its clean energy transition, the country has the second lowest share of fossil fuels in its energy supply among IEA members. It is also reducing its reliance on Russian energy imports and ensuring energy security by increasing imports from other countries, raising domestic renewable energy production and improving energy efficiency. Despite these notable successes in clean energy and energy security, significant challenges remain. Imported fossil fuels still account for over a third of the energy supply while some areas of the Finnish economy, such as transport and key industrial activities, remain dependant on fossil fuels. Also, land use change and forestry in Finland, which have historically offset a significant amount of greenhouse gas emissions, became a net source of emissions for the first time in 2021. In this report, the IEA provides a range of energy policy recommendations to help Finland smoothly manage the transition to a secure, efficient and flexible carbon neutral energy system.

Abstract: New Zealand has set ambitious targets for reducing greenhouse gas emissions, including achieving net zero emissions by 2050. The country enjoys many natural advantages for its energy transition, including an enviable renewable resource base. New Zealand already has a low‑emissions electricity system, with significant production from both hydropower and geothermal, and therefore has an attractive opportunity to leverage this clean electricity to decarbonise end-user sectors. This will require not only sizeable technological investments to efficiently electrify transport and industry, but will also necessitate a sizeable buildout of additional renewables generation capacity, along with supplemental grid and storage investments. Notably, the transport sector accounts for the highest share of emissions and is almost entirely dependent on oil. Industry is also a major contributor to New Zealand’s emissions and is heavily reliant on fossil fuels. In this report, the IEA provides energy policy recommendations to help New Zealand effectively manage the transformation of its energy sector in line with its climate targets.

Abstract: Towards Hydrogen Definitions Based on their Emissions Intensity is a new report by the International Energy Agency, designed to inform policy makers, hydrogen producers, investors and the research community in advance of the G7 Climate, Energy and Environmental Ministerial meeting in April 2023. This report assesses the greenhouse gas emissions intensity of the different hydrogen production routes and reviews ways to use the emissions intensity of hydrogen production in the development of regulation and certification schemes. An internationally agreed emissions accounting framework is a way to move away from the use of terminologies based on colours or other terms that have proved impractical for the contracts that underpin investment. The adoption of such a framework can bring much-needed transparency, as well as facilitating interoperability and limiting market fragmentation, thus becoming a useful enabler of investments for the development of international hydrogen supply chains.

Abstract: Greece aims to reduce total greenhouse gas emissions by 55% by 2030 and reach net zero emissions by 2050. It has made notable progress towards meeting these targets. This includes significantly reducing its reliance on lignite and setting a binding target to end lignite-fired power generation by 2028 while working to ensure a just transition in its lignite mining regions and reducing energy poverty. Greece has also made strong progress on renewable energy, which covered 20% of its total final energy consumption in 2021. Following Russia’s invasion of Ukraine, Greece is reducing its reliance on Russian energy imports and ensuring secure access to energy by diversifying its supply and increasing domestic energy production. Despite these notable successes, significant challenges remain. Fossil fuels still account for most of Greece’s energy use and stronger efforts are needed on energy efficiency. n this report, the IEA provides a range of energy policy recommendations to help Greece smoothly manage its transition to a secure, efficient and flexible carbon neutral energy system.

Abstract: This dataset provides statistics on electricity imports by country of origin.

Abstract: This table presents gross electricity and heat production from each energy source such as nuclear, hydro, combustible fuels, geothermal, solar, wind and other sources as well as the breakdown according to the type of plant (electricity only, CHP or heat only). Inputs by type of fuel are also provided. Gross heat production is the total heat produced by the installation and includes the heat used by the installation's auxiliaries which use a hot fluid (space heating, liquid fuel heating, etc.) and losses in the installation/network heat exchanges whereas Net Heat Production is the heat supplied to the distribution system as determined from measurements of the outgoing and return flows.

Abstract: Since the publication of its latest Power Development Plan (PDP) in 2020 (PDP 2018 Revision 1), Thailand has considerably increased its emissions reductions objectives, announcing a net zero greenhouse gas emissions target for 2065 and carbon neutrality for 2050. As the power sector is a large part of the country's emissions, and because it has a key role to play in decarbonising other sectors, meeting these targets is possible only if the power sector is decarbonising too. This report hence analyses how Thailand can achieve its clean electricity transition, by comparing the planned trajectory of the PDP with the emissions targets, and providing an assessment of the gaps. Building upon the current PDP, this report analyses how the Thai power system can decrease its emissions to meet the targets by increasing the amount of wind and solar PV in its system, and how it can integrate these variable renewable energy sources efficiently. This report concludes work area one of the joint work programme among the Electricity Generating Authority of Thailand (EGAT), the Ministry of Energy of Thailand and the International Energy Agency (IEA), and has benefited from data and input from the Thai counterparts. The analysis is based on a PLEXOS model of the Thai power system that has been developed by the IEA in cooperation with EGAT.

Abstract: This table contains data on energy supply, trade and consumption in original units (1000 metric tonnes, terajoules and gigawatt hours) for coal, oil, gas, electricity, heat, combustible renewables, biogasoline, energy, refinery gas, charcoal, waste.

Abstract: This dataset comprises a detailed set of statistics on carbon dioxide (CO2) emissions estimates from fossil fuel combustion. It includes an extensive set of products emitting CO2 from combustion analyzed in terms of their respective industry flows (iron and steel, manufacturing industries, agriculture, machinery, etc). Data are presented from 1960 onwards.

Abstract: This table contains data on the energy trade, supply and consumption of coal, oil, gas, electricity, heat, combustible renewables and waste, expressed in thousand tonnes of oil equivalent (ktoe).

Abstract: This dataset presents detailed statistics on carbon dioxide (CO2) emissions from fuel combustion by product (coal/peat, oil, gas) and flow (manufacturing, main activity producer, construction and more) according to the 2006 IPCC Guidelines. Data are presented from 1960 onwards.