MLOps

MLOps (Machine Learning Operations) is the discipline of deploying, monitoring, and managing machine learning models in production environments with the same rigor applied to traditional software systems. It bridges the gap between experimental model development and reliable production services by applying DevOps principles, including continuous integration, continuous delivery, automation, and monitoring, to the unique challenges of machine learning workflows.

An MLOps pipeline typically encompasses the entire model lifecycle: data versioning and validation, feature engineering and storage, model training and evaluation, model registry and versioning, deployment automation, inference serving, monitoring, and retraining triggers. Each stage requires specialized tooling and practices that account for the inherent differences between ML and traditional software, particularly the dependence on data quality, the need for experiment tracking, and the potential for model degradation over time.

Key MLOps components include feature stores that provide consistent feature computation across training and serving; model registries that track model versions, metadata, and lineage; serving infrastructure that handles scaling, batching, and latency requirements; monitoring systems that detect data drift, concept drift, and performance degradation; and orchestration platforms that coordinate complex training and deployment workflows. Popular tools in the ecosystem include MLflow, Kubeflow, Weights and Biases, Seldon, and cloud-native services from major providers.

Mature MLOps practices enable organizations to move from manual, ad-hoc model deployment to automated, repeatable processes that reduce time to production, improve model reliability, and support governance requirements. This is especially critical for regulated industries where model lineage, reproducibility, and audit trails are compliance requirements rather than optional best practices.