Bringing Artificial Intelligence Projects From Idea To Production

Summary

Artificial intelligence applications can provide dramatic benefits to a business, but only if you can bring them from idea to production. Henrik Landgren was behind the original efforts at Spotify to leverage data for new product features, and in his current role he works on an AI system to evaluate new businesses to invest in. In this episode he shares advice on how to identify opportunities for leveraging AI to improve your business, the capabilities necessary to enable aa successful project, and some of the pitfalls to watch out for. If you are curious about how to get started with AI, or what to consider as you build a project, then this is definitely worth a listen.

Announcements

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• Your host as usual is Tobias Macey and today I’m interviewing Henrik Landgren about his experiences building AI platforms to transform business capabilities.

Interview

• Introductions
• How did you get introduced to Python?
• Can you start by sharing your thoughts on when, where, and how AI/ML are useful tools for a business?
• What has been your experience in building AI platforms?
• For organizations who are considering investing in AI capabilities, what are some alternative strategies that they might consider first?
• What are the cases where AI is likely to be a wasted effort, or will fail to create a return on investment?
• In order to be succesful in bringing AI products to production, what are the foundational capabilities that are necessary?
  • What have you found to be a useful composition of roles and skills for building AI products?
• There are various statistics that all point to a remarkably low success rate for bringing AI into production. What are some of the pitfalls that organizations and engineers should be aware of when undertaking such a project?
• What is your strategy for identifying opportunities for a successful AI product?
  • Once you have determined the possible utility for such a project, how do you approach the work of making it a reality?
• What are the common factors in what you built at Spotify and EQT ventures?
  • Where do the two efforts diverge?
• Your work on Motherbrain is interesting because of the fact that it is dealing in what seems to be intangible or unpredictable forces. What kinds of input are you relying on to generate useful predictions?
• What are some of the most interesting, innovative, or unexpected uses of AI that you have seen?
• What are some of the biggest failures of AI that you are aware of?
• In your work at Spotify and EQT ventures, what are the most interesting, unexpected, or challenging lessons that you have learned?
• What advice or recommendations do you have for anyone who wants to learn more about the potential for AI and the work involved in bringing it to production?

Keep In Touch

• LinkedIn
• @hlandgren on Twitter

Picks

• Tobias
  • Whale
  • bat
• Henrik
  • Observable
  • Dataform
    • Data Engineering Podcast Episode

Closing Announcements

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Links

• EQT Ventures
• Stockholm Sweden
• Motherbrain
• Accenture
• Spotify
• Basic
• C#
• ASP.NET
• Javascript
• Hadoop
• McKinsey
• Deep Learning
• Data Engineer
• Data Scientist
• Machine Learning Engineer
• Discover Weekly Spotify Playlist
• GPT-3
• Deep Fakes
• DBT
  • Data Engineering Podcast Episode

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA