[] The structures of teams in bigger tech companies
8
[] Search and discovery teams in bigger tech companies
9
[] Strategy around technical debt
10
[] Promoted.ai for big marketplaces
11
[] How Andrew fits into teams
12
[] Engineering challenges when working in a small team
13
[] How much white-gloving they do amid complexity
14
[] Allowing companies to plug in their models into Promoted
15
[] Drawbacks with doing real-time streaming
16
[] Wrap up
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Explore the intricacies of MLOps in ad platforms with Andrew Yates in this 49-minute podcast episode from MLOps Coffee Sessions. Gain insights into designing ML components within larger systems, the importance of stable interfaces, and the need for intermediate ground-truth signals in ad tech. Learn about the critical balance between profitability and accuracy in advertising, and how it impacts a company's longevity. Discover Andrew's extensive experience leading ads ranking, auction, and marketplace teams at major tech companies, and his expertise in designing billion-dollar content marketplaces. Delve into topics such as the evolution of adtech, team structures in big tech companies, strategies for managing technical debt, and the engineering challenges faced by smaller teams. Understand the complexities of real-time streaming in ad platforms and the potential for companies to integrate their models into systems like Promoted.ai. This comprehensive discussion covers everything from the basics of adtech to advanced MLOps strategies, making it valuable for both newcomers and experienced professionals in the field.
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MLOps for Ad Platforms - Lessons from Ad Tech - Coffee Session 130