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Evolution and Unification of Pinterest ML Platform

Posted Mar 28, 2021 | Views 300
# Production Use Case
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SPEAKER
David Liu
David Liu
David Liu
Head of ML & Signal Platforms @ Pinterest

David is an engineering manager for ML & Signal Platforms at Pinterest. His team provides unified infrastructure for 100+ ML engineers, used in applications spanning ads, recommendations, search, and trust/safety. These large-scale systems handle datasets of billions of events per day. Previously at Pinterest, David started the recommendations and visual search teams and built one of the first ML-based recommender systems in the product. He completed his bachelor's and master's degree in computer science at Stanford

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David is an engineering manager for ML & Signal Platforms at Pinterest. His team provides unified infrastructure for 100+ ML engineers, used in applications spanning ads, recommendations, search, and trust/safety. These large-scale systems handle datasets of billions of events per day. Previously at Pinterest, David started the recommendations and visual search teams and built one of the first ML-based recommender systems in the product. He completed his bachelor's and master's degree in computer science at Stanford

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SUMMARY

As Pinterest grew over time, machine learning use cases proliferated organically across multiple teams, leading to a proliferation of technical approaches with bespoke infrastructure. The ML Platform team has been driving Pinterest Engineering to a unified platform to tame the complexity of these diverse use cases. This talk will give a brief history of the evolution of Pinterest ML and our layer-by-layer approach to standardization, including a unified feature representation, shared feature store, and standardized inference services. Finally, we share lessons learned in aligning multiple engineering orgs on a shared ML vision in the face of typical resource constraints and competing priorities.

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