MLOps Practices

MLOps brings software engineering best practices to machine learning: version control, automated testing, continuous integration, continuous deployment, and infrastructure as code. But it adapts these practices for ML's unique requirements - you're managing not just code but also data, models, experiments, and the complex interactions between them. A mature MLOps practice includes automated pipelines that take a model from training data through validation, testing, and deployment without manual intervention. It means having staging environments where models are tested with production-like data before going live. It means automated rollback when a new model performs worse than the one it's replacing. Popular MLOps tools include MLflow for experiment tracking and model management, Kubeflow for orchestrating ML pipelines on Kubernetes, and platform-specific services from the major cloud providers. The level of automation you need depends on your scale - a team deploying a handful of models can manage with lighter tooling than one maintaining hundreds. The common pitfall is treating MLOps as purely a tooling problem. Tools matter, but the harder part is establishing processes and cultural practices - code review for data pipelines, systematic experiment documentation, post-deployment monitoring as a shared responsibility. The organisations that do MLOps well treat it as a practice, not just a technology stack.