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
Dagster offers a new approach to building and running data platforms and data pipelines. It is an open-source, cloud-native orchestrator for the whole development lifecycle, with integrated lineage and observability, a declarative programming model, and best-in-class testability. Your team can get up and running in minutes thanks to Dagster Cloud, an enterprise-class hosted solution that offers serverless and hybrid deployments, enhanced security, and on-demand ephemeral test deployments. Go to dataengineeringpodcast.com/dagster today to get started. Your first 30 days are free!
Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino.
Join us at the top event for the global data community, Data Council Austin. From March 26-28th 2024, we'll play host to hundreds of attendees, 100 top speakers and dozens of startups that are advancing data science, engineering and AI. Data Council attendees are amazing founders, data scientists, lead engineers, CTOs, heads of data, investors and community organizers who are all working together to build the future of data and sharing their insights and learnings through deeply technical talks. As a listener to the Data Engineering Podcast you can get a special discount off regular priced and late bird tickets by using the promo code dataengpod20. Don't miss out on our only event this year! Visit dataengineeringpodcast.com/data-council and use code dataengpod20 to register today!
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.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com) with your story.

Links

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

Cloud Fundamentals needed for AI