Guidance
Machine learning principles
These principles help developers, engineers, decision makers and risk owners make informed decisions about the design, development, deployment and operation of their machine learning (ML) systems.
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Part 5: End of life
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At the end of the life cycle it’s important to decommission appropriately, and collate lesson’s learned.
How an asset is decommissioned at the end of life will depend on several factors. However it will likely mean archiving (for as long as is needed) or destruction. If an asset is being destroyed, this should be carried out via a process appropriate to the data the asset contained, or on which it was trained. At the end of the life cycle, it’s also important to collate lesson’s learned.