Desa aikmual

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-side languages (along with C++, Python, and Hack). But that doesn’t mean there weren’t any growing pains. Aida [...] Read More... The post The key to a happy Rust/C++ relationship appeared first on Engineering at Meta. http://dlvr.it/T8lmgy

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 model-based ranking to speed up root cause identification during investigations. Our testing has shown this new system achieves 42% accuracy in identifying root causes for investigations at their [...] Read More... The post Leveraging AI for efficient incident response appeared first on Engineering at Meta. http://dlvr.it/T8jLB2

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 be tailored to different AI models and tasks, adapted to different hardware faults, and even extended to the training phase of AI models. We’re sharing results of our own [...] Read More... The post PVF: A novel metric for understanding AI systems’ vulnerability against SDCs in model parameters appeared first on Engineering at Meta. http://dlvr.it/T8VMWQ

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-quality experience for everyone – even those who might not have the fastest connections or the latest phones. As more and more people have relied on [...] Read More... The post MLow: Meta’s low bitrate audio codec appeared first on Engineering at Meta. http://dlvr.it/T8DkVR

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 computation required to train large language models (LLMs). Traditionally, our AI model training has involved a training massive number of models that required a comparatively [...] Read More... The post How Meta trains large language models at scale appeared first on Engineering at Meta. http://dlvr.it/T8C7Gt

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 of compute and storage. A year ago, however, as the industry reached a critical inflection point due to the rise of artificial intelligence (AI), we [...] Read More... The post Maintaining large-scale AI capacity at Meta appeared first on Engineering at Meta. http://dlvr.it/T8BcRq

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 automatically fetch and apply the new value without app updates. These remotely managed configuration parameters serve various purposes such as A/B testing, feature rollout, and app [...] Read More... The post Unlocking the power of mixed reality devices with MobileConfig appeared first on Engineering at Meta. http://dlvr.it/T87k0N

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 what we call “lite” workloads that involve simple prototyping to heavier and more complex machine learning workflows. However, even though the lite [...] Read More... The post Serverless Jupyter Notebooks at Meta appeared first on Engineering at Meta. http://dlvr.it/T859wF