MLflow
Open-source platform for the entire ML lifecycle: experiment tracking, model registry, deployment, and evaluation.
MLflow is the leading open-source platform for ML lifecycle management with tracking, model registry, and deployment – developed by Databricks.
Explanation
MLflow provides four core components: Tracking (metrics/parameters), Projects (reproducible runs), Models (standard format), and Model Registry (versioning). It integrates with all major ML frameworks.
Marketing Relevance
MLflow is the de facto standard for ML lifecycle management in enterprises and startups.
Common Pitfalls
Scaling the tracking server for many teams. No built-in feature store. UI becomes slow with thousands of experiments.
Origin & History
Databricks released MLflow in 2018 as an open-source project. Version 2.0 (2023) brought MLflow Recipes and improved LLM support. Today MLflow has over 18,000 GitHub stars and is part of the Linux Foundation.
Comparisons & Differences
MLflow vs. Weights & Biases
MLflow is open-source and self-hosted; W&B is SaaS-first with better visualization but vendor lock-in.
MLflow vs. Kubeflow
MLflow focuses on experiment tracking and model management; Kubeflow on Kubernetes-native ML pipelines and orchestration.