AutoMLPipeline logo


A package that makes it trivial to create and evaluate machine learning pipeline architectures (by IBM)
+ 1

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.
AutoMLPipeline is a tool in the Machine Learning Tools category of a tech stack.
AutoMLPipeline is an open source tool with 221 GitHub stars and 20 GitHub forks. Here’s a link to AutoMLPipeline's open source repository on GitHub

AutoMLPipeline Integrations

AutoMLPipeline's 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
  • Easily extensible architecture by overloading just two main interfaces: fit! and transform!
  • Meta-ensembles that allow composition of ensembles of ensembles (recursively if needed) for robust prediction routines
  • Categorical and numerical feature selectors for specialized preprocessing routines based on types

AutoMLPipeline Alternatives & Comparisons

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

AutoMLPipeline's Followers
3 developers follow AutoMLPipeline to keep up with related blogs and decisions.