AI ENGINEER FOUNDATIONS · STAGE 06
MLOps & production deployment
Deploy, monitor, and improve models responsibly.
Learn how reproducibility, controlled deployment, monitoring, and carefully evaluated updates keep a model useful after launch.
BEFORE YOU START
Prerequisites
- Machine-learning fundamentals
- Basic deployment and pipeline concepts
LEARNING OBJECTIVES
What you should be able to do
- Track model and data lineage
- Separate service health from model quality
- Respond to drift without unsafe automatic retraining
WHAT YOU'LL COVER
Key concepts
SEE THE CONNECTIONS
A visual learning sequence
These are connected learning steps, not the only valid order. Revisit earlier ideas when an exercise reveals a gap.
Suggested learning sequence
Make runs traceable
Version code, data, configuration, and model artifacts.
Why it matters: Teams need to reproduce and explain model behavior.
Release with controls
Validate candidate quality and roll out gradually.
Why it matters: Training success does not prove production readiness.
Monitor and respond
Track service and model signals and investigate changes.
Why it matters: Operating conditions can change after launch.
Put it into practice
Monitor a forecast service for latency, input freshness, and delayed prediction error; investigate a shift before building a candidate update.
Practice tasks
- Define model and service monitoring separately.
- Describe rollback conditions for a rollout.
- Explain when retraining should not be automatic.
How you'll know you're ready
- Can describe how to reproduce a model run.
- Can distinguish input drift from measured performance decline.