implementing-mlops

MLOps infrastructure design requires strategic guidance covering experiment tracking, model registry, feature stores, deployment patterns, pipeline orchestration, and monitoring to successfully operationalize machine learning models from experimentation to production.

The best way to select MLOps platforms involves using decision frameworks to evaluate tools like MLflow, Feast, Seldon Core, and Kubeflow for your specific continuous training pipeline requirements and model governance needs.

Pipeline orchestration in MLOps coordinates the automated flow of data processing, model training, and deployment, enabling continuous training pipelines that systematically move models from experimentation to production serving environments.

You need a feature store in your ML infrastructure when operationalizing models to ensure consistent feature computation and serving across training and production environments, preventing training-serving skew and streamlining model deployment.

Implementing model monitoring and governance requires establishing frameworks that track deployed model performance, detect drift, and ensure compliance, which are critical components of the MLOps lifecycle from development through production.