When And How To Conduct An AI Program

Data Engineering Podcast

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2M ago 46:25

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Summary

Artificial intelligence technologies promise to revolutionize business and produce new sources of value. In order to make those promises a reality there is a substantial amount of strategy and investment required. Colleen Tartow has worked across all stages of the data lifecycle, and in this episode she shares her hard-earned wisdom about how to conduct an AI program for your organization.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management
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Your host is Tobias Macey and today I'm interviewing Colleen Tartow about the questions to answer before and during the development of an AI program

Interview

Introduction
How did you get involved in the area of data management?
When you say "AI Program", what are the organizational, technical, and strategic elements that it encompasses?
- How does the idea of an "AI Program" differ from an "AI Product"?
- What are some of the signals to watch for that indicate an objective for which AI is not a reasonable solution?
Who needs to be involved in the process of defining and developing that program?
- What are the skills and systems that need to be in place to effectively execute on an AI program?
"AI" has grown to be an even more overloaded term than it already was. What are some of the useful clarifying/scoping questions to address when deciding the path to deployment for different definitions of "AI"?
Organizations can easily fall into the trap of green-lighting an AI project before they have done the work of ensuring they have the necessary data and the ability to process it. What are the steps to take to build confidence in the availability of the data?
- Even if you are sure that you can get the data, what are the implementation pitfalls that teams should be wary of while building out the data flows for powering the AI system?
- What are the key considerations for powering AI applications that are substantially different from analytical applications?
The ecosystem for ML/AI is a rapidly moving target. What are the foundational/fundamental principles that you need to design around to allow for future flexibility?
What are the most interesting, innovative, or unexpected ways that you have seen AI programs implemented?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on powering AI systems?
When is AI the wrong choice?
What do you have planned for the future of your work at VAST Data?

Contact Info

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
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Links

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA