Automated Machine Learning

Automated Machine Learning is the end-to-end process of applying machine learning in automatic way.

The full autoML pipeline usually consists of:

  • data pre-processing,
  • feature engineering,
  • feature extraction,
  • feature selection,
  • model training,
  • algorithm selection,
  • hyperparameter optimization

The outlined steps can be very time-consuming. There is a lot of ML algorithms that can be applied at each step of the analysis. The difficulty in manual construction of ML pipeline lays in the difference between data formats, interfaces and computational-intensity of ML algorithms. The Automated Machine Learning solutions aims to solve this problem by checking automatically different combinations of ML algorithms. The process of automated machine learning is controlled by statistical or machine learning algorithm.

Automated machine learning in python

The list of open source python packages available:

  • auto-sklearn - python package using bayesian hyperparameter optimization with sklearn algorithms
  • TPOT - python package based on genetic programming
  • auto-keras - open source python package for neural networks architecture optimization (Neural Architecture Search)
  • auto_ml - open source python package using Keras, xgboost, LightGBM, CatBoost

Automated machine learning tools

Other than python tools for automated machine learning:

AutoML research articles

The AutoML, as well as Machine Learning in general, is still subject of active research. The notable articles in AutoML field:



The latest tutorials sent straight to your inbox.