Software Alternatives & Reviews

Machine Learning Tools

The best Machine Learning Tools based on votes, our collection of reviews, verified products and a total of 131 factors

Top Machine Learning Tools

  1. 34

    TensorFlow is an open-source machine learning framework designed and published by Google. It tracks data flow graphs over time. Nodes in the data flow graphs represent machine learning algorithms. Read more about TensorFlow.

    Open Source

  2. 24

    Algorithmia makes applications smarter, by building a community around algorithm development, where state of the art algorithms are always live and accessible to anyone.

    Open Source

  3. 33

    Keras is a minimalist, modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano.

    Open Source

  4. 26

    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.

    Open Source freemium

  5. 33

    scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

    Open Source

  6. 22

    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.

  7. 33

    Open source deep learning platform that provides a seamless path from research prototyping to...

    Open Source

  8. 21

    Machine Learning Operationalization

  9. 22

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

  10. 20

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

  11. 21

    Developer tools for deep learning research

  12. 20

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

  13. 20

    Machine Learning Operationalization

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