Introduction
Instruction and application
In ProgressIntroduction
Imagine needing to investigate a decision that a machine learning (ML) model made last year, only to find no logs, no version records and no way to recreate its predictions.
In modern MLOps, the lifecycle of an ML model doesn’t end at deployment. A well-run lifecycle involves continuous oversight, structured change tracking, and eventual decommissioning. This unit will equip you with the skills to manage the evolution and retirement of your ML models, ensuring traceability, compliance, and responsible resource management.
Why does this unit matter?
ML models are not "set and forget" assets. Over time, their utility declines, regulatory requirements change, and better alternatives emerge. Maintaining a model that is no longer accurate or necessary is not only a waste of compute resources but also a source of operational and compliance risk.
Mastering change management and decommissioning allows you to maintain a clean, efficient, and auditable model estate. It ensures that every model in production is serving a purpose and that, when its time is up, it can be retired safely and systematically.
Learning objectives
By the end of this unit, you will be able to:
Design and implement change management processes
for updating and redeploying ML models.
Set up comprehensive logging and monitoring systems
to track ML model changes and version history.
Develop model decommissioning protocols
for safely retiring and archiving obsolete ML models.
Apply ethical and regulatory considerations
to model lifecycle management and archiving.
Action item: Pause and think
Think about the models currently in use at your organisation. Do you know which version is running? Is there a plan for when they should be replaced?