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H2O

103
166
+ 1
4
Keras

906
942
+ 1
14
MLflow

122
368
+ 1
8
Decisions about H2O, Keras, and MLflow
Fabian Ulmer
Software Developer at Hestia · | 3 upvotes · 18.3K views

For my company, we may need to classify image data. Keras provides a high-level Machine Learning framework to achieve this. Specifically, CNN models can be compactly created with little code. Furthermore, already well-proven classifiers are available in Keras, which could be used as Transfer Learning for our use case.

We chose Keras over PyTorch, another Machine Learning framework, as our preliminary research showed that Keras is more compatible with .js. You can also convert a PyTorch model into TensorFlow.js, but it seems that Keras needs to be a middle step in between, which makes Keras a better choice.

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Pros of H2O
Pros of Keras
Pros of MLflow
  • 1
    Highly customizable
  • 1
    Very fast and powerful
  • 1
    Auto ML is amazing
  • 1
    Super easy to use
  • 5
    Easy and fast NN prototyping
  • 5
    Quality Documentation
  • 4
    Supports Tensorflow and Theano backends
  • 4
    Simplified Logging
  • 4
    Code First

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Cons of H2O
Cons of Keras
Cons of MLflow
  • 1
    Not very popular
  • 3
    Hard to debug
    Be the first to leave a con

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    What is H2O?

    H2O.ai 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 Keras?

    Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/

    What is MLflow?

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

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    What are some alternatives to H2O, Keras, and MLflow?
    TensorFlow
    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.
    DataRobot
    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
    scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
    PyTorch
    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.
    CUDA
    A parallel computing platform and application programming interface model,it enables developers to speed up compute-intensive applications by harnessing the power of GPUs for the parallelizable part of the computation.
    See all alternatives