engineering.fb.com
engineering.fb.com
@engineering.fb.com
Engineering at Meta - Engineering at Meta Blog
Engineering at Meta Blog
How Meta animates AI-generated images at scale - Engineering at Meta
We launched Meta AI with the goal of giving people new ways to be more productive and unlock their creativity with generative AI (GenAI). But GenAI also comes with challenges of scale. As we deploy…
Vijay Balakrishnan, Engineering at Meta Author
Manager, Capacity Engineering and Analysis
Abhishek Dhanotia, Engineering at Meta Author
Performance and Capacity Engineer
A RoCE network for distributed AI training at scale - Engineering at Meta
AI networks play an important role in interconnecting tens of thousands of GPUs together, forming the foundational infrastructure for training, enabling large models with hundreds of billions of pa…
DCPerf: An open source benchmark suite for hyperscale compute applications - Engineering at Meta
We are open-sourcing DCPerf, a collection of benchmarks that represents the diverse categories of workloads that run in data center cloud deployments. We hope that DCperf can be used more broadly b…
Meet Caddy – Meta’s next-gen mixed reality CAD software - Engineering at Meta
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 (@p…
AI Lab: The secrets to keeping machine learning engineers moving fast - Engineering at Meta
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…
Taming the tail utilization of ads inference at Meta scale - Engineering at Meta
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 …
Meta’s approach to machine learning prediction robustness - Engineering at Meta
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 reliabi…
The key to a happy Rust/C++ relationship - Engineering at Meta
The history of Rust at Meta goes all the way back to 2016, when we first started using it for source control. Today, it has been widely embraced at Meta and is one of our primary supported server-s…
Leveraging AI for efficient incident response - Engineering at Meta
We’re sharing how we streamline system reliability investigations using a new AI-assisted root cause analysis system. The system uses a combination of heuristic-based retrieval and large language m…
PVF: A novel metric for understanding AI systems’ vulnerability against SDCs in model parameters - Engineering at Meta
We’re introducing parameter vulnerability factor (PVF), a novel metric for understanding and measuring AI systems’ vulnerability against silent data corruptions (SDCs) in model parameters. PVF can …
MLow: Meta's low bitrate audio codec - Engineering at Meta
At Meta, we support real-time communication (RTC) for billions of people through our apps, including WhatsApp, Instagram, and Messenger. We are working to make RTC accessible by providing a high-q…
How Meta trains large language models at scale - Engineering at Meta
As we continue to focus our AI research and development on solving increasingly complex problems, one of the most significant and challenging shifts we’ve experienced is the sheer scale of co…
Maintaining large-scale AI capacity at Meta - Engineering at Meta
Meta is currently operating many data centers with GPU training clusters across the world. Our data centers are the backbone of our operations, meticulously designed to support the scaling demands …
Unlocking the power of mixed reality devices with MobileConfig - Engineering at Meta
MobileConfig enables developers to centrally manage a mobile app’s configuration parameters in our data centers. Once a parameter value is changed on our central server, billions of app devices aut…
Serverless Jupyter Notebooks at Meta - Engineering at Meta
At Meta, Bento, our internal Jupyter notebooks platform, is a popular tool that allows our engineers to mix code, text, and multimedia in a single document. Use cases run the entire spectrum from w…
Composable data management at Meta - Engineering at Meta
In recent years, Meta’s data management systems have evolved into a composable architecture that creates interoperability, promotes reusability, and improves engineering efficiency. We’re sharing …
Post-quantum readiness for TLS at Meta - Engineering at Meta
Today, the internet (like most digital infrastructure in general) relies heavily on the security offered by public-key cryptosystems such as RSA, Diffie-Hellman (DH), and elliptic curve cryptograph…
Behind the scenes of Threads for web - Engineering at Meta
When Threads first launched one of the top feature requests was for a web client. In this episode of the Meta Tech Podcast, Pascal Hartig (@passy) sits down with Ally C. and Kevin C., two engineers…
Loading