Many organizations struggle to transition machine learning models from development to production efficiently. The Model Factory initiative within RevOps was designed to address this challenge by creating a structured, scalable framework for model development and monitoring.
In this session, I will share real-world insights on how we built a Model Factory to streamline machine learning operations. I will walk through the implementation of feature stores to enhance model efficiency, strategies for reusability across models, and best practices for monitoring data drift and model drift. Attendees will gain practical knowledge from lessons learned, including the business analysis principles that played a key role in the success of this initiative.
Why Attend?
This talk is for data science professionals looking to improve ML operationalization at scale. You will leave with actionable takeaways on:
The role of business analysis in designing scalable AI solutions
How feature stores accelerate model development and ensure consistency
Best practices for monitoring and mitigating model drift
Lessons learned from implementing a Model Factory in a fast-paced business environment
Common pitfalls to avoid when operationalizing AI
This session is grounded in real-world experience, not theory. Expect practical insights, challenges faced, and strategies that worked—valuable takeaways you can apply immediately.
Technical level: Introductory level/students (some technical knowledge needed)
Session Length: 40 minutes
Supported by
