Machine Learning (ML) · Advanced level

Advanced level · Module 5: MLOps and Monitoring · Lesson 4 of 4

Production Readiness Review

Check an ML system against a production readiness checklist before release.

In this lesson you will

  • Apply a production readiness checklist
  • Identify and close gaps before release

Key concepts

readiness checklisttesting ML codesecurity and privacyoperational runbook

The lesson

A readiness review checks data (versioned, documented), model (evaluated, fair, documented), code (tested, reviewed), service (validated inputs, health checks, logging) and operations (monitoring, alerts, retraining, rollback, owner).

Test ML code like any code: unit tests for feature functions, tests that the pipeline handles missing and unexpected values, and a smoke test of the service.

Check security and privacy: no personal data in logs, secrets kept out of code, access to the service controlled.

Step by step

  1. Run the readiness checklist on the early-warning service.
  2. Add missing tests.
  3. Fix logging and configuration gaps.
  4. Write the operational runbook.

Practice activity

Pass the early-warning service through the readiness review.

Expected result: A completed checklist with every gap closed or accepted with a reason.

Check your understanding

  1. Name three areas of an ML readiness review.

    Show answer

    For example data, model, code, service and operations.

  2. Why test feature functions with unexpected values?

    Show answer

    Production data often contains missing or unusual values that can break the pipeline.

Taught live by an instructor — online or in person. Free course lessons are for self-study and do not lead to a certificate — see registration options.