What is AutoML?

How AutoML is changing analytics teams

What you should know?

Understanding supervised machine learning on structured data

Data engineering and ML Ops

Understanding the ML lifecycle

The challenge of ML problem definition

Which phases have been automated most successfully?

The challenge of automating data understanding

What AutoML can and can't do during data prep

AutoML's capabilities during the modeling phase

Comparing model accuracy and business evaluation

Monitoring and maintaining models

The AutoML vendor landscape

Demonstrating AutoML with KNIME

A metaphor for AutoML

Advice for team composition

Next steps