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Whether it’s iterating on Facebook’s News Feed ranking algorithm or delivering the most relevant ads to users, we are constantly exploring new features to help improve our machine learning (ML) models. Every time we add new features, we create a challenging data engineering problem that requires us to think strategically about the choices we make. [...] Read More... The post A linear programming approach for optimizing features in ML models appeared first on Facebook Engineering. http://dlvr.it/S4gMgq

MySQL, an open source database developed by Oracle, powers some of Facebook’s most important workloads. We actively develop new features in MySQL to support our evolving requirements. These features change many different areas of MySQL, including client connectors, storage engine, optimizer, and replication. Each new major version of MySQL requires significant time and effort to [...] Read More... The post Migrating Facebook to MySQL 8.0 appeared first on Facebook Engineering. http://dlvr.it/S4DVdZ

Training AI models at a large scale isn’t easy. Aside from the need for large amounts of computing power and resources, there is also considerable engineering complexity behind training very large models. At Facebook AI Research (FAIR) Engineering, we have been working on building tools and infrastructure to make training large AI models easier. Our [...] Read More... The post Fully Sharded Data Parallel: faster AI training with fewer GPUs appeared first on Facebook Engineering. http://dlvr.it/S3ndNR

For years, people have been asking us to create a true multi-device experience that allows people to use WhatsApp on other devices without requiring a smartphone connection. Today, we’re announcing the rollout of a limited public beta test for WhatsApp’s updated multi-device capability.  With this new capability, you can now use WhatsApp on your phone [...] Read More... The post How WhatsApp enables multi-device capability appeared first on Facebook Engineering. http://dlvr.it/S3kR3H

Our infrastructure supports thousands of services that handle billions of requests per second. We’ve previously discussed how we built our service encryption infrastructure to keep these globally distributed services operating securely and performantly. This post discusses the system we designed to enforce encryption policies within our network and shares some of the lessons we learned [...] Read More... The post Enforcing encryption at scale appeared first on Facebook Engineering. http://dlvr.it/S3ZzZn

What the research is: The Ribbon filter is a new data structure that is more space-efficient than the popular Bloom filters that are widely used for optimizing data retrieval. One of the ways that Bloom, and now Ribbon, filters solve real engineering problems is by providing smooth configurability unmatched by other filters. Bloom filters work [...] Read More... The post Ribbon filter: Practically smaller than Bloom and Xor appeared first on Facebook Engineering. http://dlvr.it/S3Pw13