Extending AI: From Industry to Innovation // MLOps Podcast #247 with Sophia Rowland, Senior Product Manager and David Weik, Senior Solutions Architect of SAS.

Huge thank you to SAS for sponsoring this episode. SAS - http://www.sas.com/

// Abstract

Organizations worldwide invest hundreds of billions into AI, but they do not see a return on their investments until they are able to leverage their analytical assets and models to make better decisions. At SAS, we focus on optimizing every step of the Data and AI lifecycle to get high-performing models into a form and location where they drive analytically driven decisions. Join experts from SAS as they share learnings and best practices from implementing MLOps and LLMOPs at organizations across industries, around the globe, and using various types of models and deployments, from IoT CV problems to composite flows that feature LLMs.

// Bio

Sophia Rowland

Sophia Rowland is a Senior Product Manager focusing on ModelOps and MLOps at SAS. In her previous role as a data scientist, Sophia worked with dozens of organizations to solve a variety of problems using analytics. As an active speaker and writer, Sophia has spoken at events like All Things Open, SAS Explore, and SAS Innovate as well as written dozens of blogs and articles. As a staunch North Carolinian, Sophia holds degrees from both UNC-Chapel Hill and Duke including bachelor’s degrees in computer science and psychology and a Master of Science in Quantitative Management: Business Analytics from the Fuqua School of Business. Outside of work, Sophia enjoys reading an eclectic assortment of books, hiking throughout North Carolina, and trying to stay upright while ice skating.

David Weik

David joined SAS in 2020 as a solutions architect. He helps customers to define and implement data-driven solutions. Previously, David was a SAS administrator/developer at a German insurance company working with the integration capabilities of SAS, Robotic Process Automation, and more.

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Timestamps:

[00:00] Sophia & David's preferred coffee

[00:19] Takeaways

[02:11] Please like, share, leave a review, and subscribe to our MLOps channels!

[02:55] Hands on MLOps and AI

[05:14] Next-Gen MLOps Challenges

[07:24] Data scientists adopting software

[11:48] Taking a different approach

[13:43] Zombie Model Management

[16:36] Optimizing ML Revenue Allocation

[18:39] Other use cases - Lockout - Tagout procedure

[21:43] Vision Model Integration Challenges

[26:16] Costly errors in predictive maintenance

[27:25] Integration of Gen AI

[34:32] Governance challenges in AI

[38:00] Governance in Gen AI vs Governance with Traditional ML

[41:53] Evaluation challenges in industries

[46:49] Interface frustration with Chatbots

[51:25] Implementing AI Agent's success

[54:18] Usability challenges in interfaces

[57:03] Themes in High-Performing AI Teams

[1:00:51] Wrap up