A comprehensive, hands-on tutorial series for learning AI engineering fundamentals, from development tools to production-ready AI systems. This course emphasizes practical skills using open-source tools and platforms.
This tutorial is designed for developers who want to build production-ready AI applications. Each chapter combines theoretical concepts with hands-on coding exercises, real-world examples, and practical labs.
Target Audience:
- Software engineers transitioning to AI engineering
- Data scientists wanting to productionize AI models
- Developers building AI-powered applications
- Students learning modern AI development workflows
Learning Approach:
- 📖 Conceptual explanations with real-world context
- 💻 Interactive Jupyter notebooks with coding exercises
- 🔬 Hands-on labs with step-by-step implementations
- ✅ Assessment checkpoints to verify understanding
⚠️ Common pitfalls and troubleshooting guides
Focus: Development environment setup and essential tools
Master the foundational tools for modern AI development workflows:
- Shell/SSH: Command-line operations and remote access
- VS Code: Remote development with SSH integration
- Git: Version control and collaboration
- Conda: Environment and package management
- Jupyter: Interactive computing for AI experimentation
Key Outcomes:
- Set up a complete development environment
- Connect to remote servers securely
- Navigate command-line interfaces confidently
- Manage code with Git version control
Prerequisites: Basic computer literacy, no prior programming experience required
Focus: Python fundamentals and reproducible environments
Build a solid Python foundation and learn professional environment management:
Part 1: Python Basics - Concepts
- Variables, data structures, control flow
- Functions, modules, and packages
- File I/O and JSON processing
- Exception handling and debugging
Part 2: Python Basics - Interactive Exercises
- 20+ hands-on coding exercises
- Real-world programming scenarios
- Immediate feedback and testing
Part 3: Conda Environment Management
- Creating isolated Python environments
- Package installation and dependency management
- Environment files for reproducibility
- Best practices and troubleshooting
Part 4: Advanced Environment Topics
- Mixed Conda/Pip workflows
- Jupyter kernel integration
- Environment variables and secrets management
- Docker containerization basics
Part 5: Hands-on Labs
- JSON data processing project
- Production-ready environment setup
- Debugging practice scenarios
- Reproducible research workflows
Key Outcomes:
- Write clean, idiomatic Python code
- Create and manage reproducible environments
- Debug code effectively
- Handle configuration and secrets securely
Prerequisites: Chapter 1 completion recommended
Focus: Function calling, prompt engineering, and model interfaces
Learn to build reliable, production-ready AI systems:
Part 1: Function Calling and Structured Outputs
- JSON Schema for structured responses
- Tool/function calling paradigms
- Cross-provider compatibility
- Validation and reliability techniques
Part 2: Prompt Engineering and Evaluation
- System prompts and role design
- Few-shot learning patterns
- Guardrails against hallucinations
- Parameter tuning (temperature, top_p)
- Evaluation frameworks (LLM-as-judge, human-in-the-loop)
- Production-ready prompt templates
Why Prompt Engineering Matters:
- Consistency: Eliminate unpredictable outputs (40% fewer follow-ups)
- Cost Efficiency: Reduce token usage by 50%
- Reliability: Prevent hallucinations and errors
- User Experience: Deliver the right tone and format
- Scalability: Templates that work across thousands of requests
Part 3: Model Interfaces and Deployment
- OpenAI-compatible interfaces
- HuggingFace Inference Providers
- Local inference with Ollama and vLLM
- Performance optimization strategies
- Authentication and security best practices
Key Outcomes:
- Design reliable function calling systems
- Create production-ready prompts with measurable quality
- Deploy AI models using various interfaces
- Optimize performance and cost
- Build scalable AI applications
Prerequisites: Python proficiency (Chapter 2)
Technology Stack: Python 3.10+, PyTorch 2.6.0+, CUDA 12.4+
Focus: Cloud-based and local AI model deployment
Master both cloud and local inference strategies:
Part 1: Hugging Face Platform (4 hours)
- Inference API and provider selection
- Authentication and security
- OpenAI-compatible interfaces
- Provider performance comparison
- Failover and timeout strategies
Part 2: Local Inference Endpoints (6 hours)
- Ollama installation and operation
- vLLM deployment and configuration
- OpenAI-compatible local servers
- Performance benchmarking
- Memory and hardware optimization
Key Outcomes:
- Deploy models on Hugging Face infrastructure
- Run local inference with Ollama and vLLM
- Implement failover and error handling
- Compare cloud vs. local trade-offs
- Optimize throughput and latency
Prerequisites: Chapter 3 completion
Hardware Requirements:
- Minimum: 4+ cores, 16GB RAM, 8GB VRAM
- Recommended: 8+ cores, 32GB RAM, 16GB+ VRAM
Focus: Production monitoring, debugging, and Docker deployment
Learn essential production skills for deploying and maintaining AI systems:
Part 1: Resource Monitoring and Troubleshooting (3-4 hours)
- GPU, CPU, memory, and disk monitoring
- Common error patterns and diagnostics
- Authentication and port conflict resolution
- GPU/CUDA compatibility troubleshooting
- Performance profiling and optimization
- Effective logging and debugging strategies
- Timeout and retry mechanisms
Part 2: Dockerization (5-6 hours)
- Docker fundamentals (images, containers, Dockerfile)
- Building custom images for AI/ML projects
- Multi-service orchestration with Docker Compose
- GPU support and CUDA configuration in containers
- Best practices for containerizing PyTorch applications
- Reproducible deployment workflows
Key Outcomes:
- Monitor and optimize resource utilization in AI systems
- Diagnose and resolve production deployment issues
- Create production-ready Docker images for AI applications
- Configure GPU support in containerized environments
- Orchestrate multi-service AI applications
- Implement robust error handling and monitoring
Prerequisites: Chapter 3 and 4 completion recommended
Additional Requirements:
- Docker Desktop or Docker Engine installed
- nvidia-docker2 for GPU support (if using GPU)
- Basic understanding of networking and ports
- Complete Chapter 1 - Set up your development environment
- Work through Chapter 2 - Master Python and environments
- Study Chapter 3 - Learn AI engineering fundamentals
- Deploy with Chapter 4 - Build production inference systems
- Master Chapter 5 - Production monitoring and containerization
For Experienced Python Developers:
- Skip Chapter 2 basics, review environment management only (2-3 hours)
- Focus on Chapters 3, 4, and 5 for AI-specific skills
- Estimated time: 32-40 hours
For Quick Prototyping:
- Chapter 1: Tool setup (3 hours)
- Chapter 3 Part 2: Prompt engineering (5-6 hours)
- Chapter 4 Part 1: Hugging Face deployment (4-5 hours)
- Estimated time: 12-14 hours
For Production Deployment:
- Complete all chapters sequentially
- Focus on labs, assessment checkpoints, and hands-on projects
- Extra time for experimentation and troubleshooting
- Estimated time: 55-70 hours with comprehensive practice
The course culminates in a capstone project that integrates all learned concepts. The project work begins in Week 4 and concludes with a demo in Week 8.
- Complete your project proposal this week
- Begin planning your capstone AI application
- See Week8 for detailed requirements
Build a complete AI application that demonstrates mastery of all course concepts:
- Development Environment Setup with Git and Conda
- Clean Python implementation with proper error handling
- AI System Design with function calling and prompt engineering
- Model deployment (cloud and/or local)
- Production considerations including containerization and monitoring
- Python: 3.10 or higher
- PyTorch: 2.6.0 or higher
- CUDA: 12.4 or higher (for GPU acceleration)
transformers- Hugging Face model libraryhuggingface_hub- Model and dataset accessopenai- OpenAI-compatible clientrequests- HTTP client for APIs
- VS Code - Primary IDE with remote development
- Jupyter - Interactive notebooks for experimentation
- Git - Version control
- Conda - Environment management
- Docker - Containerization (optional)
- Hugging Face - Model hosting and inference
- Ollama - Local model serving
- vLLM - High-performance inference engine
- Basic computer literacy
- Ability to follow technical instructions
- Willingness to learn command-line tools
- Programming experience in any language
- Basic understanding of machine learning concepts
- Familiarity with cloud services
- Minimum: Modern laptop/desktop, 16GB RAM, stable internet
- Recommended: GPU with 8GB+ VRAM for local inference
- Optimal: GPU with 16GB+ VRAM, 32GB+ RAM, SSD storage
By completing this tutorial series, you will be able to:
- Set up professional development environments for AI engineering
- Write production-quality Python code with proper error handling
- Design reliable AI systems using function calling and structured outputs
- Engineer effective prompts that deliver consistent, high-quality results
- Evaluate and optimize AI model performance systematically
- Deploy AI models using cloud and local infrastructure
- Build scalable applications with proper authentication and monitoring
- Monitor and optimize resource utilization in production systems
- Containerize AI applications for reproducible deployments
- Troubleshoot and debug common issues in AI development workflows
- Each chapter includes troubleshooting guides
- Common pitfalls are highlighted throughout
- Assessment checkpoints verify understanding
- Real-world examples demonstrate practical applications
This is an educational resource focused on practical AI engineering skills. Feedback and suggestions are welcome through:
- Issue reports for errors or unclear content
- Pull requests for improvements
- Suggestions for additional topics or examples
This project is licensed under the Apache License 2.0 - see the LICENSE file for details.
This tutorial emphasizes:
- Open-source first: Using accessible, community-driven tools
- Production-ready: Real-world patterns, not toy examples
- Hands-on learning: Code along, experiment, and build
- Best practices: Security, reproducibility, and maintainability
- Practical focus: Skills you'll use in actual AI engineering work
Ready to start? Begin with Chapter 1: Tool Preparation and build your AI engineering foundation!