Machine Learning Engineering
This is where AI Engineering really begins — training, evaluating and shipping real Machine Learning models, not just experimenting in a notebook. Students build the full ML engineering pipeline from raw data to a served, monitored model.
What You Will Learn
A detailed, industry-aligned breakdown of every topic covered in this module.
- Machine learning fundamentals
- Supervised learning techniques
- Unsupervised learning techniques
- Feature engineering
- Model training and evaluation
- Building ML pipelines
- Deep learning fundamentals
- Model serving and deployment
- Model monitoring and retraining
Tools You Will Use
Hands-on time with the same tools used in professional AI engineering and production ML workflows.
Python
Core programming language used across every module, from scripting to AI model development.
NumPy
Numerical computing library used for array operations and mathematical computation in ML.
Pandas
Data manipulation library used to clean, transform and analyse structured datasets.
Scikit-learn
Machine learning library used to build, train and evaluate classical ML models.
PyTorch
Deep learning framework used to build and train neural networks and deep learning models.
Jupyter
Interactive notebook environment used for data exploration, experimentation and model prototyping.
FastAPI
Modern Python web framework used to build production-grade backend and AI-serving APIs.
MLflow
Experiment tracking and model registry platform used to manage the ML model lifecycle.
Docker
Containerization platform used to package and deploy applications and AI services consistently.
Hands-On Labs
Production-style AI engineering lab scenarios, built using the same stack real AI teams ship with.
Engineer features and train supervised learning models on a real dataset.
Apply unsupervised learning techniques to uncover patterns in unlabelled data.
Build a full ML pipeline from data ingestion to model evaluation.
Serve a trained model as a FastAPI endpoint and containerize it with Docker.
Track experiments and register models using MLflow, and set up monitoring for a deployed model.
Assessment
Knowledge Assessment
Quiz covering supervised/unsupervised learning, feature engineering and model evaluation metrics.
Practical Evaluation
Students must train, evaluate, serve and monitor a Machine Learning model as a production-style service.
Projects
Industry-style deliverables added directly to your project portfolio.
Machine Learning Service
Train, evaluate and deploy Machine Learning models as production-ready services.
What This Module Builds
Students learn to develop, evaluate, package, serve and monitor Machine Learning models, and to build practical, production-ready ML services end to end.