Machine Learning Automation: A Retrospective
Machine Learning Automation: A Retrospective


Automated Machine Learning has been a hot topic in the past year, with tech companies like Google and Facebook presenting their work in AutoML publicly. There has been a significant surge in releases of open-source AutoML libraries as a consequence of the ever-growing demand for delivering complex ML models in business and research environments. In this session, we will characterise the various tools available today to automate the application of Machine Learning algorithms, and we will define their benefits compared to a more manual approach for both data scientists and analysts. We will discuss concrete real-world applications where AutoML is a potential game changer.


Andre is a Data Scientist at DataRobot, a machine learning platform that aims at democratising predictive analytics for professionals at all skill levels. A mathematician and a computer scientist by training, he remains first and foremost a practitioner experienced in a wide range of programming languages and data technologies. He has worked in data analytics in several areas (media, finance, etc.) in Paris and London, and has also contributed to an innovative research project in the field of Medical Image Analysis at the National University of Singapore.

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