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What happens when a team of mechanical engineers get tired of looking at flat images of 3D models over Zoom? Meet the team behind Caddy, a new CAD app for mixed reality. They join Pascal Hartig (@passy) on the Meta Tech Podcast to talk about teaching themselves to code, disrupting the CAD software space, and [...] Read More... The post Meet Caddy – Meta’s next-gen mixed reality CAD software appeared first on Engineering at Meta. http://dlvr.it/T9mJpz

The key to developer velocity across AI lies in minimizing time to first batch (TTFB) for machine learning (ML) engineers. AI Lab is a pre-production framework used internally at Meta. It allows us to continuously A/B test common ML workflows – enabling proactive improvements and automatically preventing regressions on TTFB.  AI Lab prevents TTFB regressions [...] Read More... The post AI Lab: The secrets to keeping machine learning engineers moving fast appeared first on Engineering at Meta. http://dlvr.it/T9gRb1

Tail utilization is a significant system issue and a major factor in overload-related failures and low compute utilization. The tail utilization optimizations at Meta have had a profound impact on model serving capacity footprint and reliability.  Failure rates, which are mostly timeout errors, were reduced by two-thirds; the compute footprint delivered 35% more work for [...] Read More... The post Taming the tail utilization of ads inference at Meta scale appeared first on Engineering at Meta. http://dlvr.it/T9QxWF

Meta’s advertising business leverages large-scale machine learning (ML) recommendation models that power millions of ads recommendations per second across Meta’s family of apps. Maintaining reliability of these ML systems helps ensure the highest level of service and uninterrupted benefit delivery to our users and advertisers. To minimize disruptions and ensure our ML systems are intrinsically [...] Read More... The post Meta’s approach to machine learning prediction robustness appeared first on Engineering at Meta. http://dlvr.it/T9PxWg