AutoML

By Diya Mathew on

 

                    AutoML



Automated machine learning(AutoML) is a process to fully automates the end-to-end of the machine learning process and makes it more usable by intended users. AutoML is also related to AI and the introduction of AutoML in Robotics is a boom in technology.

AutoML tools will outperform the manual machine learning (ML) modelling, with high robustness then only it has relevance. How to provide robustness while dealing with real-world datasets, with considerable time constraints is a relevant question in this area. TPOT, Auto-sklearn, Auto-keras and H2O-Automl are some of the open-source AutoML tools and Google cloud Automl and Microsoft AzureML are commercial AutoML Tools. To compare the robustness of the open-source AutoML tools many researchers take different datasets from openML and conduct studies based on different criteria. They use quantitative measurement based on time to understand the robustness of these AutoML tools. These experiments show, that different AutoML tools perform differently on various datasets and performance varies on different classifications. But there is no ideal tool at present. Anyway, Auto-keras, Auto-sklearn and H2O-Automl performed well. Analysing the performance of the most used AutoML tools will help improve the quality of future innovations related to this field.

General Relevance

AutoML improves machine learning processes, by reducing training time and cost, and widens its applicability. But crucial decisions are completely under the control of machines.

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