Modernized teaching repo: uv + ruff + ty only · 100% branch coverage · icontract + hypothesis provable contracts · every example transpilable to Rust via depyler and held to clippy -D warnings + proptest parity. See docs/specifications/upgrade-spec.md.

📚 Rust Data Engineering Specialization — 📦 GitHub: paiml/rust-de-specialization

• 📚 Rust From Zero
• 📚 Terminal From Zero
• 📚 ETL Pipelines with Rust
• 📚 Rust Serverless
• 📚 Rust GUI From Zero
• 📚 Linux Desktop From Zero
• 📚 Zig From Zero
• 📚 Data Ethics
• 📚 Agile With AI

📚 Next-Gen AI Development with Hugging Face Specialization — 📦 GitHub: paiml/applied-ai-engineering

• 📚 Hugging Face Hub and Ecosystem Fundamentals
• 📚 Fine-Tuning Transformers with Hugging Face
• 📚 Large Language Models with Hugging Face
• 📚 Advanced Fine-Tuning in Rust
• 📚 Production ML with Hugging Face

📚 Enterprise AI and Data Engineering with Databricks Specialization

• 📚 Databricks Lakehouse Fundamentals
• 📚 Data Engineering with Delta Lake on Databricks
• 📚 Machine Learning with Databricks and MLflow
• 📚 Generative AI and LLMs on Databricks
• 📚 Production Governance and MLOps on Databricks

📚 AI Tooling Specialization — 📦 GitHub: paiml/ai-tooling

• 📚 AWS Generative AI and Foundation Models
• 📚 AWS Intelligent Applications with Amazon Bedrock
• 📚 Prompt Architecture and NLP on Amazon Bedrock
• 📚 AI Orchestration: From Local Models to Cloud
• 📚 Enterprise AIOps with Amazon Q Business
• 📚 AI Security and Governance on AWS
• 📚 AI-Powered Analytics and Performance Engineering
• 📚 CLI Automation with Amazon Q and CloudShell
• 📚 Deterministic LLM Programming
• 📚 Agentic AI: Actor Models and Subagent Architecture
• 📚 AI Debugging and Test-Driven fixes
• 📚 Multi-Modal AI
• 📚 Privacy-Conscious Development with AI Assistants
• 📚 AI-Powered Data Pipelines with Deno
• 📚 Building Deterministic MCP Agents
• 📚 Conversational Bot Architecture with Rust and Deno
• 📚 AI Code Review Automation with GitHub Actions
• 📚 LLM Security and Vulnerabilities
• 📚 Build a Production SaaS Application with AI
• 📚 AI Tooling Capstone: Serverless Multi-Model Systems

📚 Mastering GitHub Specialization — 📦 GitHub: paiml/mastering-github

• 📚 GitHub: From Zero to Pull Request
• 📚 GitHub: Codespaces, Actions, and Ecosystem Tools
• 📚 GitHub Enterprise Administration
• 📚 GitHub: Advanced Prompt Engineering for Code
• 📚 GitHub Production Applications
• 📚 GitHub: Governing AI-Generated Code
• 📚 GitHub: Security, Identity, and Access
• 📚 GitHub: Evaluating and Integrating AI Models
• 📚 GitHub: AI-Augmented Testing and Refactoring

📚 Building Cloud Computing Solutions at Scale Specialization

• 📚 Cloud Computing Foundations
• 📚 Cloud Virtualization, Containers and APIs
• 📚 Cloud Data Engineering
• 📚 Cloud Machine Learning Engineering and MLOps

📚 MLOps | Machine Learning Operations Specialization

• 📚 Python Essentials for MLOps
• 📚 DevOps, DataOps, MLOps
• 📚 MLOps Tools: MLflow and Hugging Face
• 📚 MLOps Platforms: Amazon SageMaker and Azure ML

📚 Rust Programming Specialization

• 📚 Rust Fundamentals
• 📚 Data Engineering with Rust
• 📚 Rust for DevOps
• 📚 Python and Rust with Linux Command Line Tools
• 📚 Rust for Large Language Model Operations (LLMOps)

📚 Large Language Model Operations (LLMOps) Specialization

• 📚 Introduction to Generative AI
• 📚 Operationalizing LLMs on Azure
• 📚 Advanced Data Engineering
• 📚 GenAI and LLMs on AWS
• 📚 Databricks to Local LLMs
• 📚 Open Source LLMOps Solutions

📚 Applied Python Data Engineering Specialization

• 📚 Spark, Hadoop, and Snowflake for Data Engineering
• 📚 Virtualization, Docker, and Kubernetes for Data Engineering
• 📚 Data Visualization with Python

📚 Python, Bash and SQL Essentials for Data Engineering Specialization

• 📚 Python and Pandas for Data Engineering
• 📚 Linux and Bash for Data Engineering
• 📚 Scripting with Python and SQL for Data Engineering
• 📚 Web Applications and Command-Line Tools for Data Engineering

• 📝 Object-Oriented Programming in Python
• 📝 MySQL for Data Engineering
• 📝 Python Generators
• 📝 Python Decorator Functions
• 📝 Understand Big O Notation in Python
• 📝 Building a Bash Command-Line Tool
• 📝 Build a Static Website with Rust and Zola
• 📝 Building Rust AWS Lambda Microservices with Cargo Lambda
• 📝 Rust Secret Cipher CLI
• 📝 Rust Axum Greedy Coin Microservice
• 📝 Local LLMs with llamafile

• 🎯 Beginning Llamafile for Local Large Language Models
• 🎯 Foundations of Local Large Language Models
• 🎯 End to End LLMs with Azure

• 🚀 Master Production ML Systems - MLOps, AWS & Cloud Computing
• 📺 Watch Our Courses on edX
• 💬 Join Our Discord Community

Learn real-world ML engineering from industry experts. Used by Fortune 500 companies.

The first section is an intentionally brief, functional, data-science-centric introduction to Python. The assumption is that someone with zero programming experience can follow this tutorial and learn Python with the smallest amount of information possible.

The sections after that vary in difficulty and cover Machine Learning, Linear Optimization, build systems, commandline tools, recommendation engines, Sentiment Analysis, and Cloud Computing.

The notebook files live under notebooks/. Run them with uv run jupyter lab after make install.

• Lesson 1: Introductory Concepts
• Lesson 2: Functions
• Lesson 3: Control Structures
• Lesson 4: Intermediate Topics — Classes, Modules, Libraries
• Lesson 5: IO in Python

Single source of truth for the toolchain. No pip, no pylint, no black, no mypy, no poetry — enforced by CI grep.

Run everything: make all.

The text content of these notebooks is released under the CC-BY-NC-ND license (see license.md).