Insight · AI & ML · · 7 min read
Building Production-Ready ML Pipelines at Scale
Notebooks do not run your business. Production ML needs versioning, monitoring, and clear ownership when predictions go wrong.
Start with data contracts: freshness, schema, and lineage are not nice-to-haves for regulated or revenue-critical models.
Separate training from serving deliberately—artifacts, approvals, and rollbacks should be as routine as app deploys.
Measure business proxies in shadow before promotion; latency and drift dashboards are part of the definition of “done.”