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Lobe.ai

7
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Polyaxon

11
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+ 1
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Lobe.ai vs Polyaxon: What are the differences?

Developers describe Lobe.ai as "A simple tool for training machine learning models". It helps you train machine learning models with a free, easy to use tool. It has everything you need to bring your machine learning ideas to life. Just show it examples of what you want it to learn, and it automatically trains a custom machine learning model that can be shipped in your app. On the other hand, Polyaxon is detailed as "An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications". An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.

Lobe.ai and Polyaxon can be categorized as "Machine Learning" tools.

Polyaxon is an open source tool with 2.62K GitHub stars and 254 GitHub forks. Here's a link to Polyaxon's open source repository on GitHub.

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Pros of Lobe.ai
Pros of Polyaxon
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    • 2
      Cli
    • 2
      API
    • 2
      Streamlit integration
    • 2
      Python Client
    • 2
      Notebook integration
    • 2
      Tensorboard integration
    • 2
      VSCode integration

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    - No public GitHub repository available -

    What is Lobe.ai?

    It helps you train machine learning models with a free, easy to use tool. It has everything you need to bring your machine learning ideas to life. Just show it examples of what you want it to learn, and it automatically trains a custom machine learning model that can be shipped in your app.

    What is Polyaxon?

    An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.

    Need advice about which tool to choose?Ask the StackShare community!

    What companies use Lobe.ai?
    What companies use Polyaxon?
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      What tools integrate with Lobe.ai?
      What tools integrate with Polyaxon?
        No integrations found

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        What are some alternatives to Lobe.ai and Polyaxon?
        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.
        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.
        scikit-learn
        scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
        Keras
        Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
        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