Learn AI Engineering
Structured courses, deep-dive articles, and hands-on exercises.
Courses
All coursesAI Automation
Design and ship production AI agents and automated workflows — from ReAct loops to multi-step planners, guardrails, and observability.
Data Analysis with Python
Learn to load, clean, transform, and visualise data using the core Python data stack — NumPy, Pandas, Matplotlib, and Seaborn.
Data Science Fundamentals
Build the statistical, mathematical, and machine learning foundations every data scientist needs — from descriptive statistics through model evaluation and end-to-end pipelines.
Introduction to Large Language Models
A hands-on course for engineers who want to understand how LLMs work under the hood and build real applications with them.
Machine Learning Engineering
Build production-grade ML systems from data pipelines to live inference endpoints, with monitoring and CI/CD baked in.
RAG Engineering
Design, build, and ship production Retrieval-Augmented Generation systems that are accurate, fast, and measurably evaluated.
Deep-Dive Guides
All guidesComplete AI Development Environment Setup
Set up a professional AI engineering workspace from scratch — Python, VS Code, Jupyter, virtual environments, and your first Groq API call.
Deploying ML Models to Production
A complete playbook for taking a trained model from your laptop to a production API — serialisation, FastAPI, Docker, monitoring, and CI/CD.
Build a Production RAG Pipeline From Scratch
Go from zero to a production-ready Retrieval-Augmented Generation system — chunking, embeddings, vector search, reranking, and evaluation.
Recent Articles
All articlesBuilding Reliable AI Agents: Tool Use, Error Recovery, and State Management
A production engineer's guide to AI agents that actually work — structured tool calling, graceful error recovery, conversation state, and the hard lessons from shipping agents.
Fine-tuning vs RAG: The Engineering Decision Framework
When to fine-tune a model, when to use RAG, and when to combine them — a practical decision framework with cost analysis and real-world tradeoffs.
LLM Inference Optimization: KV Cache, Batching, and Quantization
The engineering playbook for making LLM inference fast and cheap — KV cache mechanics, continuous batching, speculative decoding, and quantization tradeoffs.
Vector Databases in Production: HNSW, IVF, and Choosing the Right Index
A deep technical comparison of HNSW and IVF vector indices — how they work, when each shines, and the operational tradeoffs that matter at scale.