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Machine Learning Operations (MLOps)

The practice of deploying, managing, and governing machine learning (ML) models, usually performed by an ML engineer.

Added Perspectives
The model operations phase includes three steps, which we call Implement, Operate, and Monitor. Our description of each step below includes an italicized summary of the key challenges for enterprises to navigate. While titles and roles vary, enterprise teams must collectively address the following tasks. The ML engineer prepares to implement the ML model in production by reviewing each model version’s features, labels, assumptions, training data, change history, and documentation. The ML and DevOps engineers tag-team with one another in this phase, dividing responsibilities based on skills and preferences. They kick off operations by scheduling, then executing the tasks that activate ML models in production workflows. Vigilant operations and response, of course, depend on the monitoring of metrics related to performance, accuracy, governance, and cost. ML engineers collaborate with ITOps engineers to track, alert, and report on these health indicators.
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