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LinkedIn Learning

Executive Guide to AutoML

via LinkedIn Learning

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Overview

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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

Reviews

4.8 rating at LinkedIn Learning based on 50 ratings

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