December 26, 2018 ☼ AI Machine Learning ☼ h2oai

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 company innovate 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.