MLOps

MLOps — machine learning operations — is the set of practices for getting ML models into production and keeping them there. It covers the full lifecycle: versioning models and training data, testing before deployment, staging and approval workflows, monitoring performance after launch, retraining when drift occurs, and retiring models when they're replaced. Without MLOps discipline, organizations frequently build working models that never make it to production, or deploy them with no process for the inevitable moment when performance degrades.

The most common failure mode in enterprise ML isn't a bad model — it's a good model with no operational infrastructure around it. Teams build, train, and validate a model, then discover there's no clear path to production: no versioning, no approval process, no monitoring, no retraining plan. The model gets deployed once and gradually becomes unreliable as conditions change. MLOps is what makes the difference between a portfolio of promising pilots and a portfolio of systems that actually make decisions in production. It's also where AI governance becomes operational — review trails, change controls, and performance accountability all live in the MLOps layer.