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This episode features a special panel discussion with Chip Huyen, co-founder and CEO of Claypot.io, and Ville Tuulos, co-founder and CEO of Outerbounds. They discuss their new books, Designing Machine Learning Systems and Effective Data Science Infrastructure, respectively, which aim to fill a growing need for state-of-the-art educational resources in ML. This episode dives into the challenges of educating data scientists, moving from prototypes to production, and understanding the full ML stack.
As part of our newly launched podcast series, we’re turning past Fireside Chats into podcast episodes because they remain incredibly valuable today. Once we catch up with these insightful discussions, we’ll continue releasing new, fresh content to keep you at the cutting edge of ML Engineering.
Key takeaways:
- Challenges in ML education and why practical resources are essential
- What data scientists need to know when transitioning from research to production
- How education fits into the broader machine learning stack
- A sneak peek into the Metaflow Sandbox, a new tool from Outerbounds to help data scientists learn production-ready ML
Outerbounds empowers organizations with a scalable, human-centric ML platform to unlock the full potential of machine learning. Learn more at Outerbounds.com.
Stay tuned as we continue releasing past and new content to help guide you on your ML Engineering journey!
