Pinterest describes how they built a production MCP (Model Context Protocol) ecosystem to enable AI agents to safely automate engineering tasks.
Pinterest evolved its Text-to-SQL system into a production Analytics Agent using unified context-intent embeddings and governance-aware ranking to serve 100,000+ tables and 2,500+ analysts.
Pinterest unified three separate ads engagement models for Home Feed, Search, and Related Pins into one shared architecture.
Pinterest investigates the online–offline discrepancy in L1 CVR models in their ads funnel.
Pinterest's Piqama is a generic quota management ecosystem that handles the full lifecycle of resource quotas across Big Data Processing and Online Services.
This post describes Pinterest's Auto Memory Retries feature for Apache Spark, which automatically retries OOM-failed tasks on larger executors to reduce failures and resource waste.
Pinterest introduced a GPU-served two-tower model using MMOE-DCN architecture for lightweight ads engagement prediction.
Pinterest describes its next-generation database ingestion framework built on CDC, Kafka, Flink, Spark, and Iceberg to replace legacy batch-based pipelines.
Spotify's ads team describes how they re-architected their serving stack to replace the Two-Tower model with more expressive neural networks capable of deep feature interactions.
Pinterest's Ads team developed transformer-based behavioral sequence models to improve ad candidate generation using users' offsite activity history.
Pinterest introduces PinLanding, a production pipeline that uses multimodal AI to automatically generate shopping collections from billions of catalog items.
Pinterest Search presents a methodology for scaling search relevance assessment using fine-tuned LLMs to replace costly human annotation.
