Deploy and Operationalize Machine Learning Models
From artifact to endpoint
Deployment turns a trained artifact plus its environment into a reproducible, callable service. Understand the components: model artifact, scoring script or entry point, environment specification, compute target, and endpoint configuration. Know the difference between real-time endpoints for low-latency interactive scoring and batch endpoints for scheduled bulk scoring, and be able to pick based on volume, latency, and cost. Understand that the scoring script must reproduce training-time preprocessing exactly and must handle malformed input gracefully.
Versioning and release strategy
Candidates should know model registration with versions, immutable artifacts, and environment pinning so a deployment can be recreated. Understand safe release patterns: deploying to a staging target, shadow or canary traffic, and traffic splitting between model versions with rollback. Autoscaling, concurrency limits, and timeout settings are operational parameters that must be tuned, not defaults.
Traps to watch for
- Deploying a model without pinning dependency versions, causing nondeterministic behavior.
- Preprocessing implemented twice, once in training and again in the endpoint, with drift between them.
- Choosing a real-time endpoint for a workload that is naturally scheduled batch.
- No rollback path or no baseline version to revert to.
- Ignoring cold-start latency and autoscale warm-up.
Worked rule of thumb
If scoring runs nightly over millions of records, use a batch endpoint; if a user waits on the response, use a real-time endpoint with a latency budget and autoscaling floor. Always deploy the new version alongside the old and shift traffic gradually.
Operational readiness
Define health checks, logging of inputs and outputs, alerting thresholds, and a documented rollback procedure before go-live.