What is MLOps?
The practice of applying DevOps principles to machine learning model lifecycle management.
MLOps (Machine Learning Operations) applies DevOps practices — CI/CD, automation, monitoring, versioning — to the machine learning lifecycle: data ingestion, model training, validation, deployment, and monitoring. Key components: experiment tracking (MLflow), model registry, feature stores, model serving, and drift detection. The goal is to deploy ML models reliably and keep them performing well in production. Kubernetes is commonly used as the runtime platform for ML workloads.
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