Module 13
MLOps and deployment
Packaging a model behind an API, tracking experiments, monitoring drift, and the product work that turns a notebook into something people use.
Outcome
What you will be able to do
You can deploy a model as a versioned, monitored service and explain its behaviour to non-technical stakeholders.
Lessons
Work through these in order
- 01Serving a model behind an APIModel artefacts, input validation, batch versus real-time inference, and the latency budget. 28 min
- 02Reproducibility, tracking and CIExperiment tracking, data and model versioning, deterministic environments and automated retraining. 26 min
- 03Monitoring, drift and incident responseWhat to monitor after launch, detecting data and concept drift, and running an ML incident. 28 min
- 04Product thinking and communicationScoping with stakeholders, presenting results without jargon, ethics and privacy, and the portfolio case study that gets you hired. 24 min
Assessment
Module quiz — 70% to pass
8 questions mixing concept checks and short code-output problems. Graded on the server with per-question explanations afterwards, unlimited retakes, and a badge with a verification code the moment you pass.