Use AutoML to create high-quality models


Short abstract
AutoML automates each step of the ML workflow so that it’s easier to use machine learning. In this session I will cover AutoGluon, library for ML practitioners seeking an open source solution, and Amazon SageMaker tools for data scientists who prefer a fully-managed service.

Full abstract
Many companies are interested in implementing Machine Learning in their application. Most of them making a decision to build a new custom model and it takes a lot of time while there are many SOTA model already available as an open source packages / libraries. AutoML is one of the concept which democratise machine learning and allows to build a strong model, without deep knowledge, in a few lines of code. Amazon invested a lot in this are by building several AWS services and open source library and this talk will be focused on them.

70% of my talk I will focus on AutoGluon (open source) library which use Stack ensembling technique to build super strong models for most of problems. This library is able to work even with Multi Modal Data (when you have text, images, tabular in a same dataset). I will cover what is Stack ensembling technique, how we implemented it and several cool features which we implemented in this library, like Adaptive Early Stoping, Inference limit, etc.

The talk was revoked