• Use cases and strategic vision   
  • Team structure    
  • Training and operationalizing data    
  • Lessons learned and looking ahead

Ready to incorporate machine learning into your products? Learn from companies at the forefront of the AI revolution. 

This interview series with early ML adopters highlights business and technical challenges across a range of industries to help you adopt ML in your organization.

How to Adopt Machine Learning

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Interviews with Technical & Business Leaders at:

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Chapter Listing

8 Chapters, Released Bi-Weekly

Chapter 1

State Auto: Technical Considerations

How the insurance leader has approached ML to drive business results, including team structure, working with data and ML tooling.

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Christine Ren
VP Predictive Modeling,

State Auto

Chapter 3

GSI Technology: Technical Considerations

George Williams
Director of APU Data Science, 

GSI Technology

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GSI’s two-stage approach to bringing together AI and memory hardware expertise involves building a multifaceted team and using proven technology.

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Chapter 2

State Auto: Business Considerations

Jason Berkey
SVP Personal Lines, 

State Auto

Why ML is key to achieving State Auto’s digital vision, using customer data to improve business processes and decision making.

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Chapter 4

GSI Technology: Business Considerations

George Williams
Director of APU Data Science, 

GSI Technology

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This memory hardware manufacturer identified an opportunity to serve their customers by enabling ML at scale.

Chapter 5

Atlassian: Technical Considerations

Jennifer Prendki
Head of Data Science, 


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How Atlassian has adopted a “Smarts-as-a-Service” strategy that empowers product teams to build their own smart features using the output of the core data science team.

Chapter 6

Atlassian: Business Considerations

Joff Redfern
VP Product, 


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The move to SaaS has provided Atlassian with a wealth of customer data, and the opportunity to use ML to create smarter products.

Chapter 7

Hydro Quebec: Technical Considerations

Arnaud Zinflou

Hydro Quebec Research Institute (IREQ)

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How the public utility approaches ML in a highly regulated industry, using multi-faceted teams and comprehensive testing.

Chapter 8

Hydro Quebec: Business Considerations

Gaétan Lantagne
Director Strategic and Transversal Projects,

Hydro Quebec Research Institute (IREQ)

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The technology vision of a leading utility company starts with where the data meets the need and creating a data-driven culture.

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