awesome-ml-experiment-management

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awesome-ml-experiment-management

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A curated list of awesome open source tools and commercial products for ML Experiment Tracking and Management 🚀

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# awesome-ml-experiment-management

A curated list of awesome open source tools and commercial products for ML Experiment Tracking and Management 🚀

* [Aim](https://github.com/aimhubio/aim): An easy-to-use and performant open-source experiment tracker.
 * [ClearML](https://github.com/allegroai/clearml): Automagical experiment tracking, environments and results
 * [Comet](https://www.comet.ml/): Manage and optimize the entire ML lifecycle, from experiment tracking to model production monitoring.
 * [DVC Studio](https://studio.iterative.ai/): A web application that works with the data, metrics and hyperparameters that you add to your ML project repositories.
 * [Guild AI](https://guild.ai/): Open source experiment tracking, pipeline automation, and hyperparameter tuning.
 * [Keepsake](https://github.com/replicate/keepsake): Version control for machine learning with support to Amazon S3 and Google Cloud Storage.
 * [mlflow](https://github.com/mlflow/mlflow): Open source platform for the machine learning lifecycle.
 * [Neptune](https://neptune.ai/): A lightweight experiment management tool that fits any workflow.
 * [Polyaxon](https://github.com/polyaxon/polyaxon):  Open-source ML experiemnts management platform.
 * [Sacred](https://github.com/IDSIA/sacred/): A tool to configure, organize, log and reproduce computational experiments.
 * [Tensorboard](https://www.tensorflow.org/tensorboard/):  Provides the visualization and tooling needed for machine learning experimentation.
 * [TraceML](https://github.com/polyaxon/traceml): Engine for ML/Data tracking, visualization, dashboards, and model UI.
 * [Weights and Biases](https://github.com/wandb/client): A tool for visualizing and tracking your machine learning experiments.

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