What is autoML?

Published on : 27 December 20213 min reading time
Since the first computer was created, the world of intelligent machines has troubled mankind. This theory is the basis for what later became known as artificial intelligence (AI). In 1990, automated Machine Learning or auto ML appeared and quietly revolutionised the world of artificial intelligence. It is designed to produce optimised learning models on its own, depending on the use. But what is auto ML?

What is auto ML?

Machine learning used to remain in the shadow of expert systems, but it has grown tremendously in the last 15 years thanks to the explosion of data and hardware advances. Now ubiquitous, the advances in popularity and their impact on business are undeniable. Machine learning or auto ML, for example, takes care of the complex tasks and steps involved in programming machine learning models. As its name suggests, this tool automates the entire processing chain of classic Machine Learning methods. The aim is to reduce the number of hyperparameters and/or to appropriate them automatically, to simplify the tools so that they can be used without being an expert in machine learning. It also helps to determine which algorithm gives the best results for concrete problems.

There are currently different technologies for using auto ML on the market. There is Google Cloud Auto ML : this is a system that works on the dual principle of improvement and unsupervised learning to manage engineering and feature production. There is also DataRobot which was created in January 2018. This platform democratises data science and automates the entire data chain, from data integration to the model deployment stage. It is powered by recent open source algorithms known to improve AI performance. Finally, there is the Knime device which allows for integrative analysis of data after processing.

What are the main benefits of auto ML?

With auto ML, which is able to automate all the procedures for implementing predictive analysis, companies can work without needing a data scientist. This is a real revolution if they are looking to achieve more efficient and effective results in the shortest time possible. Automatic ML saves a lot of time. It also provides a good basis for data preparation, avoids the use of default parameters in models, simplifies model creation and management processes, and optimises Deep Learning. In short, this tool speeds up and optimises human tasks and improves our everyday life.

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