Tecton
timezone
+00:00 GMT
SIGN IN
  • Home
  • Events
  • Content
  • Help
Sign In
Sign in or Join the community to continue

Evolution and Unification of Pinterest ML Platform

Posted Mar 28
# Production Use Case
Share
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

+ Read More

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

+ Read More
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.

+ Read More

Watch More

10
Posted May 03 | Views 36
# Organization and Processes
# Systems and Architecture
30
Posted Apr 12 | Views 22
# Data engineering
# Explainability and Observability
# Feature Stores
# Research
# Systems and Architecture