Delivering AI Systems in Highly Regulated Environments with Miriam Friedel - #653
Analysis
This podcast episode from Practical AI features Miriam Friedel, a senior director at Capital One, discussing the challenges of deploying machine learning in regulated enterprise environments. The conversation covers crucial aspects like fostering collaboration, standardizing tools and processes, utilizing open-source solutions, and encouraging model reuse. Friedel also shares insights on building effective teams, making build-versus-buy decisions for MLOps, and the future of MLOps and enterprise AI. The episode highlights practical examples, such as Capital One's open-source experiment management tool, Rubicon, and Kubeflow pipeline components, offering valuable insights for practitioners.
Key Takeaways
- •Challenges of deploying AI in regulated enterprise environments.
- •Importance of collaboration, standardized tooling, and open-source solutions.
- •Insights on building effective MLOps and the future of enterprise AI.
“Miriam shares examples of these ideas at work in some of the tools their team has built, such as Rubicon, an open source experiment management tool, and Kubeflow pipeline components that enable Capital One data scientists to efficiently leverage and scale models.”
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