Data Engineering Podcast
When And How To Conduct An AI Program
Mar 3, 2024
Colleen Tartow shares insights on conducting AI programs, emphasizing clarity in vision and business goals. The episode covers challenges in AI implementation, importance of quality data, operational shifts, and transformative potential of AI in various fields. Strategies for integrating AI systems, simplifying data pipelines, and focusing on customer benefits are also discussed.
46:25
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Quick takeaways
- Prioritize business value to ensure AI program success with clear vision and identified business needs.
- Distinguish between AI program and AI product to align technical implementation with strategic goals effectively.
Deep dives
Importance of Clear Vision and Business Value in AI Programs
Creating a successful AI program involves defining a clear vision and understanding the long-term business goal of the program. It's essential to identify the business need that the AI program will address to ensure the program's success. Prioritizing business value is crucial given the expense associated with AI initiatives. Additionally, important technical considerations include data availability, quality, volume, technical expertise, and feasibility.
Differentiating Between AI Program and AI Product
Distinguishing between an AI program and an AI product is crucial in strategy development. An AI program involves the technical implementation of algorithms, including deep learning and machine learning. On the other hand, an AI product focuses on creating business solutions using AI for end users. Understanding the difference in phrasing helps in approaching the overall solution effectively and aligning with strategic goals.
Importance of Addressing Ethical and Legal Concerns in AI Initiatives
Before embarking on an AI program, organizations need to consider ethical and legal implications. Questions about data privacy, compliance with regulations, and transparency in AI decision-making are paramount. It's vital to assess any potential legal or ethical concerns that may arise, especially in sensitive industries like healthcare, to ensure responsible and ethical use of AI technologies.
Considerations for Building Cross-Functional Teams for AI Program Success
Building a successful AI program requires a cross-functional effort involving leadership, domain experts, data scientists, data engineers, and software engineers. Domain experts provide insights into specific problem domains, while technical experts implement the technical solutions. Involvement of stakeholders, clear success criteria, and interdisciplinary collaboration are essential for effective AI program execution.
8.
Emphasizing Simplification and Value in Data Pipelines and AI Program Implementation
2min
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
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](https://www.dataengineeringpodcast.com/data-council) and use code **dataengpod20** to register today! Promo Code: dataengpod20
- Starburst:  This episode is brought to you by Starburst - a data lake analytics platform for data engineers who are battling to build and scale high quality data pipelines on the data lake. Powered by Trino, Starburst runs petabyte-scale SQL analytics fast at a fraction of the cost of traditional methods, helping you meet all your data needs ranging from AI/ML workloads to data applications to complete analytics. Trusted by the teams at Comcast and Doordash, Starburst delivers the adaptability and flexibility a lakehouse ecosystem promises, while providing a single point of access for your data and all your data governance allowing you to discover, transform, govern, and secure all in one place. 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? Try Starburst Galaxy today, the easiest and fastest way to get started using Trino, and get $500 of credits free. [dataengineeringpodcast.com/starburst](https://www.dataengineeringpodcast.com/starburst)
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