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Neptune brings organization and collaboration to data science projects. All the experiement-related objects are backed-up and organized ready to be analyzed and shared with others. Works with all common technologies and integrates with other tools.

Track and version your notebooks Log all your notebooks directly from Jupyter or Jupyter Lab. All you need is to install a Jupyter extension.

Manage your experimentation process Neptune tracks your work with virtually no interference to the way you like to do it. Decide what is relevant to your project and start tracking: - Metrics - Hyperparameters - Data versions - Model files - Images - Source code

Integrate with your workflow easily Neptune is a lightweight extension to your current workflow. Works with all common technologies in data science domain and integrates with other tools. It will take you 5 minutes to get started.

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  3. Comet lets you track code, experiments, and results on ML projects. It’s fast, simple, and free for open source projects.

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  5. Managed MLflow is built on top of MLflow, an open source platform developed by Databricks to help manage the complete Machine Learning lifecycle with enterprise reliability, security, and scale.

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  7. The 5Analytics AI platform enables you to use artificial intelligence to automate important commercial decisions and implement digital business models.

  8. Datatron automates the deployment, monitoring, governance, and validation of your machine learning models in scikit-learn, TensorFlow, Keras, Pytorch, R, H20 and SAS

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  10. Pachyderm is an open source analytics engine that uses Docker containers for distributed computations.

  11. Datmo tools help power up your existing model workflow. A new standard built by data scientists, for data scientists.

  12. Iterative removes friction from managing datasets and ML models and introduces seamless data scientists collaboration.

  13. Seldon increases engagement and revenue by providing a smarter personalised user experience. Reviews

  1. User avatar
    Easy to use, not overdone, good for model management and collab

    Only negative is I didn't see it integrated with Azure, does with Google, AWS and one more. Looks real nice, and pretty powerful and plenty useful features for a data science group

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Categories: Data Science And Machine Learning, Data Science Notebooks, Machine Learning Tools, Machine Learning