Summary
In this episode of the AI Engineering Podcast machine learning engineer Shashank Kapadia explores the transformative role of generative AI in retail. Shashank shares his journey from an engineering background to becoming a key player in ML, highlighting the excitement of understanding human behavior at scale through AI. He discusses the challenges and opportunities presented by generative AI in retail, where it complements traditional ML by enhancing explainability and personalization, predicting consumer needs, and driving autonomous shopping agents and emotional commerce. Shashank elaborates on the architectural and operational shifts required to integrate generative AI into existing systems, emphasizing orchestration, safety nets, and continuous learning loops, while also addressing the balance between building and buying AI solutions, considering factors like data privacy and customization.


Announcements

• Hello and welcome to the AI Engineering Podcast, your guide to the fast-moving world of building scalable and maintainable AI systems
• Your host is Tobias Macey and today I'm interviewing Shashank Kapadia about applications of generative AI in retail

Interview

• Introduction
• How did you get involved in machine learning?
• Can you summarize the main applications of generative AI that you are seeing the most benefit from in retail/ecommerce?
• What are the major architectural patterns that you are deploying for generative AI workloads?
• Working at an organization like WalMart, you already had a substantial investment in ML/MLOps. What are the elements of that organizational capability that remain the same, and what are the catalyzed changes as a result of generative models?
• When working at the scale of Walmart, what are the different types of bottlenecks that you encounter which can be ignored at smaller orders of magnitude?
• Generative AI introduces new risks around brand reputation, accuracy, trustworthiness, etc. What are the architectural components that you find most effective in managing and monitoring the interactions that you provide to your customers?
• Can you describe the architecture of the technical systems that you have built to enable the organization to take advantage of generative models?
• What are the human elements that you rely on to ensure the safety of your AI products?
• What are the most interesting, innovative, or unexpected ways that you have seen generative AI break at scale?
• What are the most interesting, unexpected, or challenging lessons that you have learned while working on AI?
• When is generative AI the wrong choice?
• What are your paying special attention to over the next 6 - 36 months in AI?

Contact Info

• LinkedIn

Parting Question

• From your perspective, what are the biggest gaps in tooling, technology, or training for AI systems today?

Closing Announcements

• Thank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.
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Links

• Walmart Labs

The intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0