Sophia and David from SAS discuss challenges in MLOps, integrating generative AI, transitioning to real-time processes, and empowering business users with AI innovation. They also explore obstacles in moving AI models to production, collaboration between data scientists and engineers, and common themes in high-performing ML AI teams.
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insights INSIGHT
Shift to Real-Time MLOps
MLOps is shifting from batch processing to real-time integration requiring data scientists to work directly with application developers.
This shift demands new skills for data scientists unfamiliar with APIs, CI/CD, and production environments.
volunteer_activism ADVICE
Involve Data Scientists in API Integration
Show data scientists how API-based deployment differs from batch and involve them in building simple front-end apps.
Reducing required input fields helps smooth integration and reduces production deployment hurdles.
question_answer ANECDOTE
Healthcare Capstone Handoff Regret
Sophia shared a healthcare capstone project where the team handed off training code instead of a usable model.
This flawed handoff left the healthcare organization struggling to use the analytics effectively.
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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.
David Weik has a passion for data and creating integrated customer-centric solutions. Thinking data and people first to create value-added solutions.
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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http://www.sas.com/
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Connect with David on LinkedIn: https://www.linkedin.com/in/david-weik/
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