H2O Driverless AI Demo

Name: H2O Driverless AI Demo
Uploaded: 2022-03-18
Duration: 6 min
Description: A demo of H2O Driverless AI for supervised learning: connect a data source, explore and visualize data, split for testing, and launch an AutoML experiment.

Observability
H2O Driverless AI

Overview

This demo walks through H2O Driverless AI, an automated machine learning platform for supervised learning problems that runs in the cloud or on premise. It covers the full workflow: connecting to a data source, exploring and visualizing a dataset, splitting data for training and holdout testing, configuring and launching an AutoML experiment, and reviewing the resulting models. The demo also covers the machine learning interpretability module and options for deploying a finished model into production.

Key takeaways

How to connect to data sources, including a JDBC connector for SQL data warehouses, and review column-level details and distributions before modeling
How the visualize step surfaces interesting correlations and outliers across your dataset automatically
How to split data for training and holdout testing, including stratified splitting for rare events and time-based splitting
How the accuracy, time, and interpretability settings control experiment complexity, feature engineering, and cross-validation
How the machine learning interpretability module uses SHAP values, partial dependence plots, and disparate impact analysis to explain and check a model for bias

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