Skip to content

AutoML

자동화된 기계 학습은 기계 학습을 실제 문제에 적용하는 작업을 자동화하는 프로세스입니다. AutoML에는 원시 데이터 세트에서 시작하여 배포 준비가 된 기계 학습 모델 구축에 이르기까지 모든 단계가 잠재적으로 포함됩니다.

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

에이다넷 (AdaNet)
전문가 개입을 최소화하면서 고품질의 모델을 자동으로 학습시키기 위한 가벼운 텐서플로우 기반 프레임워크다.
오토케라스 (Auto-Keras)
텍사스 A&M에서 개발된 자동 머신러닝을 위한 오픈소스 소프트웨어 라이브러리로, 딥러닝 모델의 아키텍처 및 초매개변수를 자동으로 검색하기 위한 함수를 제공한다.
Neural Network Intelligence (NNI)
마이크로소프트가 제공하는 툴킷으로, 사용자가 머신러닝 모델(예를 들어 초매개변수), 신경망 아키텍처 또는 복잡한 시스템의 매개변수를 효율적이고 자동화된 방식으로 설계, 튜닝하는 데 도움이 된다.

See also

Favorite site

About