Summary
This blog post discusses the implementation of request-level deduplication techniques at Pinterest, significantly enhancing the efficiency of their recommendation systems across storage, training, and serving. By aggregating user sequence data and optimizing data processing, Pinterest achieved up to a 100x model scale-up while maintaining acceptable infrastructure costs. Key approaches highlighted include the use of Apache Iceberg for storage compression, Synchronization of Batch Normalization to preserve model quality, and the development of a Deduplicated Cross-Attention Transformer to boost training and serving speeds. Overall, these techniques not only improve performance but also allow for sustainable scaling of machine learning models.