درباره این اپیزود
Russell Brooks, Principal Machine Learning Engineer at Realtor.com, joins Hugo Bowne-Anderson to discuss what it takes to build an enterprise ML platform from scratch. Russell shares his experiences at OpCity and Realtor.com, where he successfully scaled their ML operations from a small team to reproducible, automated ML systems. Learn the key questions to ask when building an ML platform, how to demonstrate the impact of ML within an organization, and the best practices for collaboration between software engineers, data scientists, platform engineers, and ML engineers.
We’re excited to bring you our newly launched podcast, where we take past Fireside Chats and convert them into podcast episodes because their insights remain incredibly valuable today. Once we’ve released these archived episodes, we’ll move on to fresh, new content to keep you up to date with the latest in ML Engineering.
Key takeaways:
- Practical insights into building an enterprise ML platform from the ground up
- How to demonstrate the impact of ML functions in an organization
- Effective collaboration strategies across engineering and ML roles
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 both past and new content to help guide you on your ML Engineering journey!
