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The Current State of Automated Machine Learning

@machinelearnbot

I have previously attempted to capture AutoML's essence as follows: If, as Sebastian Raschka has described it, computer programming is about automation, and machine learning is "all about automating automation," then automated machine learning is "the automation of automating automation." Follow me, here: programming relieves us by managing rote tasks; machine learning allows computers to learn how to best perform these rote tasks; automated machine learning allows for computers to learn how to optimize the outcome of learning how to perform these rote actions. It also happens to be the winner of KDnuggets' recent automated data science and machine learning blog contest. You can read the Auto-sklearn development team's winning blog submission to the recent KDnuggets automated data science and machine learning blog contest here, as well as a follow-up interview with the developers here.


The Current State of Automated Machine Learning

#artificialintelligence

Automated Machine Learning (AutoML) has become a topic of considerable interest over the past year. A recent KDnuggets blog competition focused on this topic, resulting in a handful of interesting ideas and projects. Several AutoML tools have been generating notable interest and gaining respect and notoriety in this time frame as well.


The Current State of Automated Machine Learning

#artificialintelligence

Automated Machine Learning (AutoML) has become a topic of considerable interest over the past year. A recent KDnuggets blog competition focused on this topic, resulting in a handful of interesting ideas and projects. Several AutoML tools have been generating notable interest and gaining respect and notoriety in this time frame as well. This post will provide a brief explanation of AutoML, argue for its justification and adoption, present a pair of contemporary tools for its pursuit, and discuss AutoML's anticipated future and direction. We can talk about what automated machine learning is, and we can talk about what automated machine learning is not.



Automated Machine Learning (AutoML) Libraries for Python

#artificialintelligence

AutoML provides tools to automatically discover good machine learning model pipelines for a dataset with very little user intervention. It is ideal for domain experts new to machine learning or machine learning practitioners looking to get good results quickly for a predictive modeling task. Open-source libraries are available for using AutoML methods with popular machine learning libraries in Python, such as the scikit-learn machine learning library. In this tutorial, you will discover how to use top open-source AutoML libraries for scikit-learn in Python. Automated Machine Learning (AutoML) Libraries for Python Photo by Michael Coghlan, some rights reserved.