Master’s Program
MLOps & Production AI
Deploy, monitor and iterate on real AI systems
Overview
This track is about turning models into production systems. You learn containerization, CI/CD basics, monitoring signals (performance/drift), and safe iteration practices with versioned releases.
Key outcomes
- Containerize and deploy model services
- Track experiments and model versions
- Define monitoring signals and retraining triggers
- Adopt a production-ready workflow with CI/CD fundamentals
Format
- Engineering-oriented labs
- Remote-friendly (worldwide cohorts)
Tools
- Docker
- FastAPI
- GitHub Actions basics
- MLflow basics
Detailed program
Production-first MLOps: ship model services with versioning, CI/CD basics, monitoring signals and safe iteration practices used by real teams.
Module 1 — Production Foundations (Reproducibility & Packaging)
Turn notebooks into reliable, reproducible codebases.
1–2 weeks
Module 1 — Production Foundations (Reproducibility & Packaging)
Turn notebooks into reliable, reproducible codebases.
What you’ll learn
- Project structure: src/, configs, environments, reproducibility
- Dependency management and lockfiles (why they matter)
- Training vs inference parity (same transforms, same schemas)
- Testing basics: unit tests for transforms and validation
Skills you’ll gain
Module 2 — Model Serving (FastAPI patterns)
Serve models like products: schemas, validation, stability.
2–3 weeks
Module 2 — Model Serving (FastAPI patterns)
Serve models like products: schemas, validation, stability.
What you’ll learn
- FastAPI endpoints: predict, health, version info
- Input validation and payload schemas (prevent bad requests)
- Performance basics: batching awareness, serialization, caching basics
- Observability basics: logs, metrics, request tracing mindset
Skills you’ll gain
Module 3 — Containerization (Docker for ML services)
Package your service to run the same everywhere.
2 weeks
Module 3 — Containerization (Docker for ML services)
Package your service to run the same everywhere.
What you’ll learn
- Dockerfile best practices (small images, reproducibility)
- Environment configuration (dev/prod parity, secrets awareness)
- Local orchestration basics (compose mindset if needed)
- Build/run/debug workflow
Skills you’ll gain
Module 4 — Experiment Tracking & Model Versioning (MLflow basics)
Track runs, artifacts and manage model versions correctly.
2 weeks
Module 4 — Experiment Tracking & Model Versioning (MLflow basics)
Track runs, artifacts and manage model versions correctly.
What you’ll learn
- Tracking runs: params, metrics, artifacts
- Model packaging: signature, input schema, reproducibility
- Versioning discipline: tags, stages, release notes mindset
- Rollback mindset and reproducible retraining
Skills you’ll gain
Module 5 — CI/CD Fundamentals for ML
Automate checks so you can ship safely.
1–2 weeks
Module 5 — CI/CD Fundamentals for ML
Automate checks so you can ship safely.
What you’ll learn
- Linting + tests + basic quality gates
- Build pipeline basics (Docker build, push, versioning)
- Deployment triggers and environment separation mindset
- Model checks: schema checks, basic regression tests
Skills you’ll gain
Module 6 — Monitoring, Drift & Iteration
Know when a model degrades and what to do next.
2 weeks
Module 6 — Monitoring, Drift & Iteration
Know when a model degrades and what to do next.
What you’ll learn
- Monitoring signals: latency, error rates, data drift, performance proxies
- Drift basics: what it is, why it happens, what to track
- Retraining triggers and feedback loop design
- Incident response basics: triage, rollback, mitigation plan
Skills you’ll gain
Capstone — Production-ready ML API
Deliver a deployable ML service with versioning, tests and monitoring plan.
2–3 weeks
Capstone — Production-ready ML API
Deliver a deployable ML service with versioning, tests and monitoring plan.
What you’ll learn
- Package a trained model + API + Docker image
- Add tests + basic CI pipeline
- Document deployment + monitoring signals
- Deliver a demo and technical README
Skills you’ll gain