#128
B
Rank #183
MLflow
An open-source platform for tracking ML experiments, packaging models, and managing a model registry across the ML lifecycle. Standout trait is broad framework-agnostic adoption as a de facto standard for experiment tracking.
62.0
/ 100
⚙ Full Stack
A Python tracking server that can run with a file-based backend or a proper Postgres/MySQL backend store, with artifacts pushed to local disk, S3, or GCS. No first-party official Docker image — most deployments use community-maintained images.
📈 Scaling Analysis
The tracking server itself is fairly lightweight and scales adequately for typical team-scale experiment volume once backed by a real DB instead of the file-store default, but the well-known lack of built-in multi-tenant auth is the real limiter for scaling to a shared, internet-facing deployment.
🚀 Running on Nexlayer
Single app pod plus a Postgres pod (`postgres.pod:5432`) for the backend store, with artifact storage either on a persistent volume or an external bucket. There's no built-in auth, so a Nexlayer-managed proxy pod handling authentication in front of it is necessary before exposing it beyond internal use.