What Is MLOps?
MLOps (machine learning operations) is the practice of deploying, monitoring, and continuously maintaining machine learning models in production so they stay accurate and reliable over time.
MLOps (machine learning operations) is the set of practices for deploying, monitoring, and maintaining machine learning models in production — so models stay accurate and reliable as data and conditions change.
Why it matters
A model that works at launch will silently degrade as the world shifts (a phenomenon called drift). MLOps catches that decay through monitoring and automated retraining. Without it, ML projects either never reach production or quietly stop working after they do — a leading cause of failed AI projects.
What it includes
Automated deployment pipelines, model and data monitoring, drift detection, retraining workflows, versioning, and alerting — the operational backbone that turns a model into a dependable system.
Related
See machine learning consulting and data engineering.
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