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Igel

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Lobe.ai

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Lobe.ai vs Igel: 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, Igel is detailed as "A CLI tool to run machine learning without writing code". It is a delightful machine learning tool that allows to train, test and use models without writing code.

Lobe.ai and Igel belong to "Machine Learning Tools" category of the tech stack.

Some of the features offered by Lobe.ai are:

  • Machine learning made easy
  • Free and Private
  • Ship Anywhere

On the other hand, Igel provides the following key features:

  • Supports all state of the art machine learning models (even preview models)
  • Supports different data preprocessing methods
  • Provides flexibility and data control while writing configurations
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What is Igel?

It is a delightful machine learning tool that allows to train, test and use models without writing code.

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.

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

What tools integrate with Igel?
What tools integrate with Lobe.ai?
    No integrations found
    What are some alternatives to Igel and Lobe.ai?
    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