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AutoML

lastmod 2026-10-27created 2019-12-069 edits

Automated machine learning is the process of automating the task of applying machine learning to real-world problems. AutoML potentially covers every stage, from starting with a raw dataset to building a machine learning model that is ready for deployment.

What is AutoML

Automated Machine Learning provides methods and processes to make Machine Learning available for non-Machine Learning experts, to improve efficiency of Machine Learning and to accelerate research on Machine Learning.

Machine learning (ML) has achieved considerable successes in recent years and an ever-growing number of disciplines rely on it. However, this success crucially relies on human machine learning experts to perform the following tasks:

  • Preprocess and clean the data.
  • Select and construct appropriate features.
  • Select an appropriate model family.
  • Optimize model hyperparameters.
  • Postprocess machine learning models.
  • Critically analyze the results obtained.

As the complexity of these tasks is often beyond non-ML-experts, the rapid growth of machine learning applications has created a demand for off-the-shelf machine learning methods that can be used easily and without expert knowledge. We call the resulting research area that targets progressive automation of machine learning AutoML.

Examples of AutoML

Research in Automated Machine Learning is very diverse and brought up packages and methods targeted at both researchers and end users.

AutoML systems

Throughout recent years several off-the-shelf packages have been developed which provide automated machine learning. While there are more packages than the one listed below, we restrict ourselves to a subset of the most well-known ones:

  • AutoWEKA is an approach for the simultaneous selection of a machine learning algorithm and its hyperparameters; combined with the WEKA package it automatically yields good models for a wide variety of data sets.
  • Auto-sklearn is an extension of AutoWEKA using the Python library scikit-learn which is a drop-in replacement for regular scikit-learn classifiers and regressors.
  • TPOT is a data-science assistant which optimizes machine learning pipelines using genetic programming.
  • H2O AutoML provides automated model selection and ensembling for the H2O machine learning and data analytics platform.
  • TransmogrifAI is an AutoML library running on top of Spark.
  • MLBoX is an AutoML library with three components: preprocessing, optimisation and prediction.

Implementations

AdaNet
A lightweight TensorFlow-based framework for automatically training high-quality models with minimal expert intervention.
Auto-Keras
An open-source software library for automated machine learning developed at Texas A&M. It provides functions for automatically searching for the architecture and hyperparameters of deep learning models.
Neural Network Intelligence (NNI)
A toolkit from Microsoft that helps users design and tune machine learning models (e.g., hyperparameters), neural network architectures, or the parameters of complex systems in an efficient, automated way.

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