The Role of Analytics in Shaping the Future of MLOps

Jan 22, 2025

Sophia Rowland, a Senior Product Manager at SAS with a robust background in data science, shares insights on integrating AI and analytics in operational settings. She tackles the challenges of dependency management caused by siloed IT and data science teams, and the psychological impact of algorithms on user motivation. Sophia also discusses the importance of aligning technical and business perspectives for effective MLOps, while emphasizing the significance of data ethics and responsible AI practices.

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ANECDOTE

From Data Science to Product Management

Sophia Rowland transitioned from data science to product management at SAS due to a recurring problem observed with clients.
Organizations struggled to operationalize machine learning models effectively, leading to her focus on ModelOps.

INSIGHT

Defining ModelOps and MLOps

ModelOps and MLOps definitions are fluid, varying across sources.
ModelOps focuses on the business application of analytics for better decisions, encompassing MLOps' technical aspects.

INSIGHT

Dependency Management Challenges

Dependency management issues arise when IT and data science teams work in silos, leading to mismatches between development and production environments.
Containerization using tools like Docker and Kubernetes offers a solution by packaging model dependencies for easier deployment.

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Sophia Rowland, Senior Product Manager at SAS, discusses her journey from data science to product management at SAS, focusing on the integration of AI and analytics. She explains the concepts of Model Ops and ML Ops, the challenges organizations face in operationalizing machine learning models, and the critical role of analytics in this process.

Key Takeaways:

Dependency management errors that occur when IT and data science teams work in silos
The connection between algorithms and psychology, using data and software to tap into motivation
How to discern hype from meaningful advancements in emerging technologies
The influence of user behavior on AI adoption
Ways to stay updated in the rapidly evolving field of AI

Guest Bio: Sophia Rowland is a Senior Product Manager focusing on ModelOps and ML Ops 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 the AI Summit, All Things Open, SAS Explore, and SAS Innovate; she has also written dozens of articles and blog posts. As a lifelong 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.

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