Achieving Near-Linear Training Scalability for Pinterest’... - 惯性聚合

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Stories by Pinterest Engineering on Medium

Beyond Two Towers: Launching the 3-Tower Engagement Co-Train Model (Part 2) Evolving Pinterest’s Embedding Retrieval Platform Building Pinterest’s VLM Serving Stack on NVIDIA Dynamo Becoming an AI Team Scaling Conditional Learned Retrieval for Pinterest Home Feed Pinner Progression: Better Use-Case Representation Driving Weekly Active User Growth at Pinterest Securing Infrastructure at Scale: Introducing Pinterest’s Resource Provisioner Pipeline (RPP) Automated Schema Evolution in Pinterest’s Next-Generation DB Ingestion Framework Making User-Sequence Data More Cost-Efficient, Faster, and Easier to Use An Engineer’s Guide to Better AI Skills: Implementing a Testing Process to Optimize Agent… Enhancing Ad Relevance: Integrating Real-Time Context into Sequential Recommender Models Optimizing ML Workload Network Efficiency (Part I): Feature Trimmer From Clicks to Conversions: Architecting Shopping Conversion Candidate Generation at Pinterest Smarter URL Normalization at Scale: How MIQPS Powers Content Deduplication at Pinterest Finding zombies in our systems: A real-world story of CPU bottlenecks Scaling Recommendation Systems with Request-Level Deduplication Performance for Everyone Evolution of Multi-Objective Optimization at Pinterest Home feed

Achieving Near-Linear Training Scalability for Pinterest’...

Pinterest En · 2026-06-26 · via Stories by Pinterest Engineering on Medium

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