Month: July 2024

In our Engineering Energizers Q&A series, we explore the paths of engineering leaders who have attained significant accomplishments in their respective fields. Today, we spotlight Dima Statz, Director of Software Engineering at Salesforce, who leads the development of Salesforce’s new Speech-to-Text (STT) service. STT leverages advanced speech recognition technology to provide real-time, accurate transcriptions of… Continue reading How Salesforce’s New Speech-to-Text Service Uses OpenAI Whisper Models for Real-Time Transcriptions

In our “Engineering Energizers” Q&A series, we delve into the journeys of distinguished engineering leaders. Today, we feature Archana Kumari, Director of Software Engineering at Salesforce. Archana leads our India-based Data Cloud Compute Layer team, which played a pivotal role in a recent transition from Amazon EC2 to Kubernetes for Trino workloads. This shift not… Continue reading Data Cloud’s Lightning-Fast Migration: From Amazon EC2 to Kubernetes in 6 Months

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… Continue reading Meet Caddy – Meta’s next-gen mixed reality CAD software

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… Continue reading AI Lab: The secrets to keeping machine learning engineers moving fast

In our “Engineering Energizers” Q&A series, we delve into the journeys of engineering leaders who have made notable strides in their areas of expertise. This edition features Adithya Vishwanath, Vice President of Software Engineering at Salesforce. He leads the Data Cloud team, a pivotal platform that integrates diverse data sources, offering real-time insights and streamlined… Continue reading The Unstructured Data Dilemma: How Data Cloud Handles 250 Trillion Transactions Weekly

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… Continue reading Taming the tail utilization of ads inference at Meta scale

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… Continue reading Meta’s approach to machine learning prediction robustness

In our Engineering Energizers Q&A series, we explore engineers who have pioneered advancements in their fields. Today, we meet Rahul Singh, Vice President of Software Engineering, leading the India-based Data Cloud team. His team is focused on delivering a robust, scalable, and efficient Data Cloud platform that consolidates customer data to enhance business insights and… Continue reading Unlocking Data Cloud’s Secret for Scaling Massive Data Volumes and Slashing Processing Bottlenecks