Master’s Program
Applied ML Engineer
Modeling + engineering to ship reliable ML features
Overview
This track is designed for people who want to build ML features that survive in real systems. You focus on practical ML, evaluation, inference constraints, and engineering patterns used to deliver stable model services.
Key outcomes
- Build robust ML baselines and iterate fast with clean experiments
- Design inference pipelines with performance & reliability in mind
- Deploy services and manage versioning correctly
- Write maintainable ML code with tests and reusable modules
Format
- Hands-on labs + deliverables
- Remote-friendly (worldwide cohorts)
Tools
- Python, scikit-learn
- FastAPI
- Docker
- Git/GitHub
- MLflow basics
Detailed program
Engineering-oriented ML: build reliable model features with strong evaluation, clean architecture and deployment constraints in mind.
Module 1 — ML Baselines & Evaluation Discipline
Build strong baselines and evaluate correctly before optimizing.
2 weeks
Module 1 — ML Baselines & Evaluation Discipline
Build strong baselines and evaluate correctly before optimizing.
What you’ll learn
- Pipeline setup: preprocessing, splits, cross-validation
- Metrics selection by use case + thresholding basics
- Error analysis: slices, robustness checks, leakage detection
- Experiment discipline: clean comparisons and tracking habits
Skills you’ll gain
Module 2 — Training/Inference Architecture
Design code that works both in training and production inference.
2–3 weeks
Module 2 — Training/Inference Architecture
Design code that works both in training and production inference.
What you’ll learn
- Train/inference parity and feature pipelines
- Reusable transforms and data contracts
- Model packaging and input schemas
- Performance basics: batch inference mindset
Skills you’ll gain
Module 3 — Deployment Patterns (Service-first)
Ship ML as an API with stability and maintainability.
2–3 weeks
Module 3 — Deployment Patterns (Service-first)
Ship ML as an API with stability and maintainability.
What you’ll learn
- FastAPI endpoints + validation + version route
- Dockerizing the service for consistent runtime
- Logging and simple observability practices
- Rollback mindset and safe iteration
Skills you’ll gain
Module 4 — Production Quality (Testing & Maintainability)
Make your ML codebase maintainable like real engineering teams.
1–2 weeks
Module 4 — Production Quality (Testing & Maintainability)
Make your ML codebase maintainable like real engineering teams.
What you’ll learn
- Unit tests for preprocessing and business rules
- Basic integration tests for API payloads
- Refactoring patterns and clean module boundaries
- Documentation: README, usage, constraints, known limitations
Skills you’ll gain
Capstone — ML Feature Service
Deliver a stable ML service ready for integration in real systems.
2–3 weeks
Capstone — ML Feature Service
Deliver a stable ML service ready for integration in real systems.
What you’ll learn
- Build an end-to-end ML pipeline + API + Docker image
- Add tests and a minimal CI workflow
- Document integration usage and model behavior
- Demo + portfolio packaging
Skills you’ll gain