MLOps

Full Definition

MLOps (Machine Learning Operations) is the practice of applying DevOps principles—automation, continuous integration and delivery, monitoring, and collaboration—to machine learning model development, deployment, and lifecycle management. The gap between machine learning research (where models are trained in notebooks and evaluated on test datasets) and production ML (where models must reliably serve predictions at scale, maintain accuracy over time as data distributions shift, and be retrained and redeployed without service disruption) is where MLOps operates. Without MLOps, machine learning projects frequently fail to generate business value: models that perform well in development fail in production because of training-serving skew (differences between how features are computed during training and serving), data quality issues, or model degradation as the real-world data distribution shifts from the training distribution. MLOps encompasses four primary operational domains. Model training pipelines: automated, reproducible workflows for data preparation, feature engineering, model training, and evaluation that can be triggered on schedule or by data change events. Model deployment: packaging models as APIs or batch processing jobs with appropriate versioning, A/B testing infrastructure, and rollback capability. Model monitoring: continuous tracking of model prediction quality (accuracy, drift, performance metrics) and data quality (input feature distributions, anomaly detection) in production to detect degradation before it causes business harm. Feature stores: centralized repositories of computed features that prevent training-serving skew by ensuring the same feature computation logic is used in both training and serving. MLOps maturity levels reflect the degree of automation and operational rigor applied to ML workflows. Level 0 (manual): data scientists manually perform all steps; models are deployed infrequently. Level 1 (ML pipeline automation): training pipelines are automated; continuous training on new data is possible, but model deployment is still manual. Level 2 (CI/CD pipeline automation): both training and deployment are automated; changes to any component trigger automated testing and deployment, enabling rapid experimentation and reliable production deployment of the best-performing model. Most enterprise organizations are at Level 0-1; Level 2 is the target state for organizations where ML is a core business capability.

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