ML Engineer
Move a reviewed model from an experiment to a versioned serving endpoint.
Prepare reviewable inputs for an AI-assisted workflow
Run the mortgage kit and inspect the income discrepancy. Define which source fields an assistant may use, which statements require evidence, and where a human must review its recommendation.
Start with the browser demo or download the local kit. For the workspace exercise, you need approved access to the relevant Genedata capabilities and a reviewer for your output.
Practice tasks
0 / 4 completed
Notes and progress are saved on this device.
Turn a candidate into a dependable service.
Move a reviewed model from an experiment to a versioned serving endpoint.
Review the candidate run, input schema, dependencies, and evaluation evidence with the data scientist.
Package the model and record the registered version and its source artifacts.
Validate the model and save a drift baseline before serving. Confirm resource and access requirements.
Deploy through the agreed release process, verify endpoint behavior, and pass monitoring and recovery details to MLOps.
Expected handoff
A validated model version, serving endpoint, and operational handoff.
Explore your platform capabilitiesMeasure your progress
- Lead time from approved candidate to deployment
- Releases missing validation or drift-baseline evidence
Use AI to draft packaging or validation code; test it against the actual runtime and input contract.