mlop
Experiment tracking for machine learning
mlop is an open source MLOps platform for tracking, optimizing, and collaborating on machine learning experiments. It addresses the need to monitor model accuracy and performance metrics over time, watch parameters and gradients during training, and maintain reproducibility through experiment, model version, and git status tracking, including uncommitted files. Real-time alerts and email notifications flag critical issues and performance changes.
The platform includes experiment tracking with parameter, gradient, and multi-media logging, plus alerting and notifications. It integrates with existing codebases through a Python client, and is compatible with the Weights & Biases API to support migration. Compute is available, while inference is listed as coming soon. The project is developed in the open on GitHub.
mlop is aimed at machine learning engineers and teams building and deploying models. It is packaged as a free tier for personal use or small teams with one seat and limited storage, a Pro plan for businesses with up to ten seats, larger storage, and email support, and a custom Enterprise edition with unlimited seats, 24/7 support, a self-hosted option, security audit, and a private Slack channel. Logging hours are unlimited across plans.
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