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Aquarium

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Igel vs Aquarium: What are the differences?

Developers describe Igel 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. On the other hand, Aquarium is detailed as "*Improve Your ML Dataset Quality *". Machine learning models are only as good as the datasets they're trained on It helps ML teams make better models by improving their dataset quality..

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

Some of the features offered by Igel are:

  • 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

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

  • Upload your dataset to get a health check of its quality, quantity, and diversity. Zoom in and out of your dataset. Uncover distribution biases before you train. Find and fix labeling errors quickly
  • Upload model inferences against your labeled datasets and deep dive into its performance. Find where your model is performing well and badly so you can take the best actions to improve it
  • With knowledge of your dataset diversity and model performance, it automatically samples the best data to sample to label and retrain on. Your model performance just gets better
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What is Aquarium?

Machine learning models are only as good as the datasets they're trained on. It helps ML teams make better models by improving their dataset quality.

What is Igel?

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

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

What tools integrate with Aquarium?
What tools integrate with Igel?
    No integrations found
    What are some alternatives to Aquarium and Igel?
    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.
    Keras
    Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
    scikit-learn
    scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
    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