AI Engineering — From models to useful systems

AI Engineering connects AI knowledge with useful products. This 200-hour course moves from foundational AI and model concepts to building and evaluating practical systems. Data, Python, language models and source-grounded answers form the core of the path. Project work considers answer quality, security, limitations and deployment. The goal is to design a system for a defined need and assess how well it works.

A selection of what you will learn

1. 01

   Data and feature engineering for real problems

2. 02

   Building and evaluating predictive ML models

3. 03

   Neural networks and deep learning training

4. 04

   Transformer architecture and language models

5. 05

   Prompt design, structured outputs and tool calling

6. 06

   Embeddings, semantic search and vector databases

7. 07

   RAG design and grounded answers from company data

8. 08

   Model adaptation, fine-tuning and LoRA

9. 09

   Agents, intelligent workflows and tool permissions

10. 10

    Deployment, evaluation, security and AI monitoring …

Plus additional topics tailored to the course