What's new in Driverless AI?

Arno, H2O’s CTO, gave a great 1+ hour overview in what’s new with Driverless AI version 1.4.1. If you check back in a few weeks/months, it’ll be even better. In all honesty, I have never seen a companyinnovate this fast. Below are my notes from the video: H2O-3 is the open source product Driverless AI is the commercial product Makes Feature Engineering for you When you have Domain Knowledge, Feature Engineering can give you a huge lift Salary, Jon Title, Zip Code example What about people in this Zip Code, with # of cars >> generate mean of salaries Create out of fold estimates Don’t take your own prediction feature for training Writes in Python, CUDA and C++ is under the hood that Python directs Able to create good models in an automated way Driverless AI does not handle images Handles strings, numbers, and categorial Can be 100’s of Gigabytes Creates 100’s of models with 1,000’s of new features Creates an ensemble model after its done Then creates a exportable model (Java runtime or Python) C++ version is being worked on All standalone models Connect with Python client or via the web browser Changelog is on docs.h2o.ai Tests against Kaggle datasets BNP Paribas Kaggle set, Driverless AI ranked in the top 10 out of the box Took Driverless AI 2 hours, whereas Grandmasters it took 2 months Discussed how Logloss is interpreted Uses Reusable Holdout(RH) and subsamples of RH Driverless AI uses unsupervised methods to make supervised models Uses XGBoost, GLM, LightGBM, TensorFlow CNN, and Rule Fit Implemented in R’s datatable for feature engineering and munging Working on a open source version of R’s datatable in Python Overview in how Driverless AI handles outliers (AutoViz) AutoViz only plots what you should see, not 100’s of scatterplots like Tableau Overview on the GUI, what you can do Validation and Test sets. How to use them and when Checks data shift in training and testing set Includes Machine Learning Interpretability suite Does Time Series and NLP And much more! Arno’s presentation style is excellent and he makes Data Science simply understood.