Automated Machine Learning and Explainability

Explore automated machine learning and explainability in this 24-minute conference talk from CloudWorld 2022. Dive into Oracle AutoML's capabilities in reducing barriers to advanced machine learning application. Learn about the need for automated ML explainability (MLX) techniques as pipelines and models become more complex. Discover Oracle AutoMLx, a novel AutoML pipeline integrating explainability techniques for a seamless user experience. Gain insights into the data scientist pipeline, algorithm selection, model tuning, and adaptive data reduction. Examine Oracle AutoML Benchmarking and feature importance examples using the Titanic dataset. Investigate feature dependence examples with three or more features. Get an overview of logistics machine learning capabilities in Oracle Transportation Management and Global Trade Management Cloud. Understand how AutoML is implemented in Oracle Machine Learning.

Intro
The data scientist pipeline
Algorithm selection & model tuning
Adaptive data reduction
Oracle AutoML Benchmarking
Feature Importance examples-titanic dataset
Feature dependence examples-three+ features
Oracle Transportation Management and Global Trade Management Cloud
Logistics machine learning capabilities overview
AutoML in Oracle Machine Learning