Building a Production CI/CD Pipline for Machine Learning Models Across Distributed Industrial Plants

Key Takeaways

• Deploying an ML model to a fleet of physically separate industrial plants is not the same problem as deploying to a fleet of cloud regions: the deployable unit has to be the model plus its site-specific configuration, versioned together, not the model alone.
• A validation gate which only looks at aggregate model quality against a generic hold out set will pass models that are inaccurate for a certain site’s sensor calibration or operating range; validation has to be site aware in order to protect a live process.
• Staged, canary-style rollout, a pilot site first, observed, then the remaining fleet, gives a multi-site deployment the same safety property canary releases give a web service, applied to a setting where the “user” is a physical process rather than a browser session.
• It is to say updating a site with a specific version of what is known to be a good...

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