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Skyl.ai vs AutoMLPipeline: What are the differences?

What is Skyl.ai? Manage your Complete Machine Learning Workflow. Build & deploy ML models faster on unstructured data. No specialized skills required Easy-to-use & scalable SaaS platform..

What is AutoMLPipeline? A package that makes it trivial to create and evaluate machine learning pipeline architectures (by IBM). It is a package that makes it trivial to create complex ML pipeline structures using simple expressions. It leverages on the built-in macro programming features of Julia to symbolically process, manipulate pipeline expressions, and automatically discover optimal structures for machine learning prediction and classification.

Skyl.ai and AutoMLPipeline belong to "Machine Learning Tools" category of the tech stack.

Some of the features offered by Skyl.ai are:

  • Collect, label, and visualize unstructured data
  • Guided modules to upload, clean, label, and visualize unstructured data
  • Create & train models automatically

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

  • Pipeline API that allows high-level description of processing workflow
  • Common API wrappers for ML libs including Scikitlearn, DecisionTree, etc
  • Symbolic pipeline parsing for easy expression of complexed pipeline structures
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What is AutoMLPipeline?

It is a package that makes it trivial to create complex ML pipeline structures using simple expressions. It leverages on the built-in macro programming features of Julia to symbolically process, manipulate pipeline expressions, and automatically discover optimal structures for machine learning prediction and classification.

What is Skyl.ai?

Build & deploy ML models faster on unstructured data. No specialized skills required. Easy-to-use & scalable SaaS platform.

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

What tools integrate with AutoMLPipeline?
What tools integrate with Skyl.ai?
    No integrations found
    What are some alternatives to AutoMLPipeline and Skyl.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