Getting Started with H2O Driverless AI

Name: Getting Started with H2O Driverless AI
Uploaded: 2022-03-18
Duration: 57 min
Description: A high-level tour of Driverless AI alongside open source H2O-3 and Sparkling Water, walking through its seven core components before a live demo.

Predictive AI
H2O Driverless AI

Overview

This webinar, hosted by H2O.ai's marketing and partnerships engineering teams, gives a high-level tour of Driverless AI alongside H2O's open-source H2O-3 and Sparkling Water platforms. It walks through Driverless AI's seven core components (Datasets, Projects, AutoViz, Experiments, Diagnostics, MLI, and Deployments) before a live demo that loads a credit card default dataset, runs an AutoML experiment, reviews model interpretability, and deploys the resulting model as a REST server. It closes with audience Q&A covering handling of text and OCR data.

Key takeaways

The difference between H2O's products: H2O-3 and Sparkling Water (open-source, library-style) versus Driverless AI (the enterprise AutoML platform)
What each of Driverless AI's seven components does: Datasets, Projects, AutoViz, Experiments, Diagnostics, MLI (machine learning interpretability), and Deployments
How to load a dataset, split it into train and test sets, and use AutoViz to spot correlations and potential leakage before running an experiment
How the accuracy, time, and interpretability knobs in an experiment control search space, runtime, and how explainable the resulting feature transformations are
How MLI surrogate models (like decision trees and linear models), Shapley plots, and disparate impact analysis help explain what a Driverless AI model is doing and why, which matters for regulated use cases

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