MLOps

MLOps, short for Machine Learning Operations, is the set of practices and tooling for deploying, monitoring, versioning, and continuously retraining machine learning models in production. It brings DevOps discipline to the model life cycle, treating data, features, and models as versioned artifacts that move through automated pipelines rather than one-off scripts handed from data scientists to engineers.

• Model deployment - Packaging trained models and exposing them for inference, most often behind an API.
• Monitoring and drift detection - Watching accuracy, latency, and data distribution to catch models that degrade over time.
• Versioning and reproducibility - Tracking datasets, features, code, and model weights so any prediction can be traced and rebuilt.
• Continuous training - Automating retraining and safe rollout as new data arrives.

In real API operations MLOps and API management converge at the serving layer: a model is only useful once it is reachable as a governed, documented, and rate-limited API, described with something like OpenAPI and held to the same security and versioning standards as any other endpoint. That convergence matters even more in the agentic turn, where models and the tools they call are wired together through APIs, and the reliability of an AI system depends on the operational rigor MLOps provides.