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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• 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

• LinkedIn

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

• VAST Data


• Colleen's Previous Appearance


• Linear Regression


• CoreWeave


• Lambda Labs


• MAD Landscape
  
  
  
  
  • Podcast Episode
  
  
  • ML Episode
  
  

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

Sponsored By:

• Dagster: 
  
  Data teams are tasked with helping organizations deliver on the premise of data, and with ML and AI maturing rapidly, expectations have never been this high. However data engineers are challenged by both technical complexity and organizational complexity, with heterogeneous technologies to adopt, multiple data disciplines converging, legacy systems to support, and costs to manage.
  
  Dagster is an open-source orchestration solution that helps data teams reign in this complexity and build data platforms that provide unparalleled observability, and testability, all while fostering collaboration across the enterprise. With enterprise-grade hosting on Dagster Cloud, you gain even more capabilities, adding cost management, security, and CI support to further boost your teams' productivity. Go to [dagster.io](https://dagster.io/lp/dagster-cloud-trial?source=data-eng-podcast) today to get your first 30 days free!
• Data Council: 
  
  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](ht