mlop

mlop is an open source experiment tracking platform built for machine learning engineers who want full visibility into how their models train and perform. It covers the core loop of ML development: log metrics, track parameters and gradients, capture media outputs, and compare runs across experiments.

It's compatible with the Weights & Biases API, so teams already using W&B can migrate without rewriting their logging code. That's a practical differentiator for anyone looking to move off a proprietary tool without friction.

Key capabilities include:

• Parameter and gradient tracking logged in real-time as the model trains, so you can watch learning dynamics as they happen
• Multi-media logging for images and other outputs directly alongside numerical metrics
• Reproducibility support by capturing git commit hashes and flagging uncommitted files per run, so you always know the exact state of your code
• Real-time alerts via email for critical issues and performance drops, keeping you informed without constant dashboard-watching
• Experiment comparison across runs with named projects and configurable metadata

The platform is self-hostable and community-driven, which matters for teams with data residency requirements or those who want to avoid vendor lock-in on a core part of their ML workflow. Unlike observability tools focused on LLM tracing or inference monitoring, mlop targets the training side of ML: the iteration loop where you tune hyperparameters, compare model architectures, and debug learning behavior.

It's aimed at ML engineers and research teams who run frequent experiments and need structured tracking without paying for a managed service.