MACHINE LEARNING ENGINEER ROADMAP · STAGE 04 OF 04
Deployment and MLOps
Package a model behind a service and plan for monitoring, safe updates, and recovery.
Ship an inference workflow with explicit interfaces, operational checks, and a rollback plan.
CORE SKILLS
Key concepts in deployment and mlops
Model serving interfaces
Expose predictions through a stable interface that validates requests and returns predictable responses.
Topics include
- Batch and online inference
- Input and output contracts
- Latency and throughput
Reproducible model releases
Track model, code, configuration, and data assumptions so a deployed version can be identified and reviewed.
Topics include
- Artifact and version tracking
- Dependency and environment capture
- Release checks
Monitoring and drift
Separate service health from model quality and investigate distribution changes before deciding how to respond.
Topics include
- Latency, errors, and availability
- Input and prediction monitoring
- Delayed labels and performance
Safe updates and rollback
Evaluate candidate models and release changes gradually with clear criteria for stopping or reverting.
Topics include
- Staging and shadow evaluation
- Canary releases
- Rollback and incident learning
APPLY WHAT YOU LEARN
Practical project
Package the earlier model as a small prediction API, add request validation and health checks, and document monitoring signals and rollback criteria.
CHECK YOUR UNDERSTANDING
You are ready to move on when
- Can distinguish a healthy endpoint from a useful model.
- Can identify what changed between releases and how to recover.