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Pros of H2O
Pros of MLflow
Pros of numericaal
  • 1
    Highly customizable
  • 1
    Very fast and powerful
  • 1
    Auto ML is amazing
  • 1
    Super easy to use
  • 3
    Code First
  • 3
    Simplified Logging
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    Cons of H2O
    Cons of MLflow
    Cons of numericaal
    • 1
      Not very popular
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        What is H2O? is the maker behind H2O, the leading open source machine learning platform for smarter applications and data products. H2O operationalizes data science by developing and deploying algorithms and models for R, Python and the Sparkling Water API for Spark.

        What is MLflow?

        MLflow is an open source platform for managing the end-to-end machine learning lifecycle.

        What is numericaal?

        numericaal automates model optimization and management so you can focus on data and training.

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        Jobs that mention H2O, MLflow, and numericaal as a desired skillset
        San Francisco, CA, US; Palo Alto, CA, US; Seattle, WA, US
        San Francisco, CA, US; Palo Alto, CA, US; Seattle, WA, US
        What companies use H2O?
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        What companies use numericaal?
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          What tools integrate with H2O?
          What tools integrate with MLflow?
          What tools integrate with numericaal?
            No integrations found
            What are some alternatives to H2O, MLflow, and numericaal?
            TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
            It is an enterprise-grade predictive analysis software for business analysts, data scientists, executives, and IT professionals. It analyzes numerous innovative machine learning algorithms to establish, implement, and build bespoke predictive models for each situation.
            scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
            PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.
            Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano.
            See all alternatives