Learn about AutoML, the opportunities and challenges that arise in attempting to automate machine learning, and how this automation affects your organization.

Syllabus

Introduction

How AutoML is changing analytics teams
What you should know?

1. Introducing AutoML

What is AutoML?
Understanding supervised machine learning on structured data
Data engineering and ML Ops
Understanding the ML lifecycle
The challenge of ML problem definition

2. Stages in the ML Lifecycle

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

3. AutoML Options

The AutoML vendor landscape
Demonstrating AutoML with KNIME
A metaphor for AutoML
Advice for team composition

Conclusion

Next steps

Taught by

Keith McCormick

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