What is MLOps?
Practices for deploying, monitoring, and managing machine learning models in production.
MLOps (Machine Learning Operations) is a set of practices that combines ML, DevOps, and data engineering to deploy and maintain ML models in production reliably and efficiently. It covers the full ML lifecycle: data versioning, experiment tracking, model training pipelines, automated testing, deployment, monitoring for model drift, and retraining triggers. Organizations with mature MLOps practices can iterate on models faster and deliver more reliable AI-powered products.
Related terms
An organization's preparedness to successfully adopt and deploy artificial intelligence.
The degree to which data is accurate, complete, consistent, timely, and fit for its intended use.
An automated workflow that extracts, transforms, and loads data from sources to destinations.
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