DescriptionBuilding machine learning systems remains something of a (black) art, requiring a lot of prior experience to compose appropriate ML workflows and their hyperparameters. To democratize machine learning, and make it easily accessible to those who need it, we need a more principled approach to experimentation to understand how to build machine learning systems and progressively automate this process as much as possible. First, we created OpenML, an open science platform allowing scientists to share datasets and train many machine learning models from many software tools in a frictionless yet principled way. It also organizes all results online, providing detailed insight into the performance of machine learning techniques, and allowing a more scientific, data-driven approach to building new machine learning systems. Second, we use this knowledge to create automatic machine learning (AutoML) techniques that learn from these experiments to help people build better models, faster, or automate the process entirely.
|Period||15 Jun 2018|
|Event title||SIGMOD 2018 Workshop on Data Management for End-to-End Machine Learning|
|Degree of Recognition||International|