Cohort Program
Machine Learning Engineering: From Jupyter to Production
Eight weeks from notebook to reproducible, production-ready ML pipelines
About this cohort program
Most machine learning courses stop at the notebook. This one starts where the notebook ends. You will build real ML pipelines from raw data to trained model to production API, using the engineering practices that separate professional ML teams from notebook experimenters.
What you will learn
- Build end-to-end ML pipelines with scikit-learn and PyTorch that are reproducible, versioned, and testable
- Design feature engineering and data preprocessing workflows that scale beyond your laptop
- Apply cross-validation, hyperparameter tuning, and model selection with statistical rigour
- Package and serve a trained model as a production API with monitoring, logging, and drift detection
Who this is for
- Software engineers transitioning into machine learning who want production-grade foundations, not just theory
- Data scientists who have built models in notebooks and need to learn the engineering layer
- ML practitioners whose work stops at prototypes and who want to ship to production with confidence
By the end
Before
Notebooks that cannot leave your laptop
After
End-to-end ML pipelines that run reliably in production
Before
Models that work in dev but break in deployment
After
Production ML with monitoring, drift detection, and CI/CD
Before
Experiments no one else can reproduce
After
Versioned, trackable experiments with MLflow audit trails
Syllabus
Session 1 · Saturday, June 6, 2026
The ML Engineering Mindset: Data Pipelines and Feature Engineering
Session 2 · Saturday, June 13, 2026
Model Selection, Cross-Validation, and Hyperparameter Tuning
Session 3 · Saturday, June 20, 2026
Gradient Boosting: XGBoost, LightGBM, and Introduction to PyTorch
Session 4 · Saturday, June 27, 2026
Neural Networks: Architecture, Training, and Regularisation
Session 5 · Saturday, July 4, 2026
Transfer Learning, Pre-Trained Models, and Model Evaluation
Session 6 · Saturday, July 11, 2026
Experiment Tracking with MLflow and Serving ML Models
Session 7 · Saturday, July 18, 2026
Containerisation, CI/CD, and Production Monitoring
Session 8 · Saturday, July 25, 2026
Feature Stores, Scaling, Cost Optimisation, and Capstone
About Dr.
Dr. Sarah Lin
Principal ML Research Scientist, former Google Brain
Vetted by Maram
Sarah spent five years at Google Brain working on large-scale supervised and semi-supervised learning systems before joining an ML research lab in London. She holds a PhD in Machine Learning from Carnegie Mellon and has co-authored over 20 peer-reviewed papers. She teaches ML Engineering at Imperial College London as a visiting lecturer.
View full profile →What learners say
Reviews appear here once 3 learners have completed this session.