Getting ML models into reliable production is where most teams struggle. MLOps frameworks address this by covering five core areas: experiment tracking, model versioning and registry, workflow orchestration, model serving, and monitoring. The guide surveys the most widely adopted tools — MLflow (modular, beginner-friendly, open-source), Kubeflow (Kubernetes-native, suited for deep learning at scale), Metaflow (developer-experience-focused, Netflix-born), DVC (Git-style versioning for data and models), and general orchestrators like Airflow, Prefect, and Dagster. It also covers managed cloud platforms (AWS SageMaker, Azure ML, Databricks) and touches on emerging LLMOps requirements for large language model workflows. Selection guidance is provided based on team size, infrastructure, and ML maturity.
Table of contents
Why MLOps Frameworks Exist — and What They Actually SolveCore Components of Any MLOps FrameworkMLflow: The Open-Source MLOps StandardKubeflow: Kubernetes-Native MLOpsMetaflow: Human-Centric ML PipelinesSort: