Scalable Automated Machine learning with H2O in Python

AutoML is fundamentally changing the face of ML-based solutions. H2O is a fully open-source, machine learning platform that tends to automate the repetitive tasks in ML so that Data Scientists can focus more on the problem rather than the models. AutoML is a step towards democratizing ML.

The demand for machine learning systems has soared over the past few years. This is majorly due to the success of Machine Learning techniques in a wide range of applications. AutoML is fundamentally changing the face of ML-based solutions today by enabling people from diverse backgrounds to use machine learning models to address complex scenarios. However, even with a clear indication that machine learning can provide a boost to certain businesses, a lot of companies today struggle to deploy ML models.

H2O is a fully open-source, distributed in-memory machine learning platform with linear scalability. H2O supports the most widely used statistical & machine learning algorithms, including gradient boosted machines, generalized linear models, deep learning, and many more. H2O also has an industry-leading AutoML functionality that can be used for automating the machine learning workflow, which includes automatic training and tuning of many models within a user-specified time-limit.

The purpose of this talk is to provide an overview of the field of Automatic Machine Learning and introduce the AutoML functionality in H2O. H2O AutoML can be used for automating the machine learning workflow, which includes automatic training and tuning of many models within a user-specified time-limit.

H2O AutoML is available in all the H2O interfaces including the h2o R package, Python module and the Flow web GUI. This talk, however, will be focussed on the Python client. We will also provide simple code examples to get you started using AutoML.

A basic understanding of the Machine learning concepts like model building will be good, however not mandatory.

As to why I am the best person to talk on this topic is partly because I work for a company that works on this product. I work closely with the immensely talented people who are working on this AutoML solution to make it seamless for people.