Achieving Near-Linear Training Scalability for Pinterest’s Foundation Models - 惯性聚合

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Stories by Pinterest Engineering on Medium

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’s Foundation Models

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

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