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Meta

OCP Summit 2024: The open future of networking hardware for AI

At Open Compute Project Summit (OCP) 2024, we’re sharing details about our next-generation network fabric for our AI training clusters.

Meta

Meta’s open AI hardware vision

At the Open Compute Project (OCP) Global Summit 2024, we’re showcasing our latest open AI hardware designs with the OCP community.

Pinterest

Pinterest Tiered Storage for Apache Kafka®️: A Broker-Decoupled Approach

Meta

Inside Bento: Jupyter Notebooks at Meta

This episode of the Meta Tech Podcast is all about Bento, Meta’s internal distribution of Jupyter Notebooks, an open-source web-based comput...

Meta

RETINAS: Real-Time Infrastructure Accounting for Sustainability

We are introducing a new metric— real-time server fleet utilization effectiveness —as part of the RETINAS initiative to help reduce emission...

Meta

Aparna Ramani discusses the future of AI infrastructure

Delivering new AI technologies at scale also means rethinking every layer of our infrastructure – from silicon and software systems and even...

Meta

Meta’s approach to machine learning prediction robustness

Meta’s advertising business leverages large-scale machine learning (ML) recommendation models that power millions of ads recommendations per...

Meta

Leveraging AI for efficient incident response

We’re sharing how we streamline system reliability investigations using a new AI-assisted root cause analysis system.

Meta

PVF: A novel metric for understanding AI systems’ vulnerability against SDCs in model parameters

We’re introducing parameter vulnerability factor (PVF), a novel metric for understanding and measuring AI systems’ vulnerability against sil...

Meta

How Meta trains large language models at scale

As we continue to focus our AI research and development on solving increasingly complex problems, one of the most significant and challengin...

Meta

Serverless Jupyter Notebooks 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 ...

Meta

Composable data management at Meta

In recent years, Meta’s data management systems have evolved into a composable architecture that creates interoperability, promotes reusabil...

Meta

Logarithm: A logging engine for AI training workflows and services

Systems and application logs play a key role in operations, observability, and debugging workflows at Meta.

Meta

AI debugging at Meta with HawkEye

HawkEye is the powerful toolkit used internally at Meta for monitoring, observability, and debuggability of the end-to-end machine learning ...

Pinterest

Running Unified PubSub Client in Production at Pinterest

Meta

Automating data removal

Meta’s Systematic Code and Asset Removal Framework (SCARF) has a subsystem for identifying and removing unused data types.

Meta

Automating dead code cleanup

Meta’s Systematic Code and Asset Removal Framework (SCARF) has a subsystem for identifying and removing dead code.

Meta

Automating product deprecation

Systematic Code and Asset Removal Framework (SCARF) is Meta’s unused code and data deletion framework.

Meta

Arcadia: An end-to-end AI system performance simulator

We’re introducing Arcadia, Meta’s unified system that simulates the compute, memory, and network performance of AI training clusters.

Meta

Scheduling Jupyter Notebooks at Meta

At Meta, Bento is our internal Jupyter notebooks platform that is leveraged by many internal users.

Meta

Building and deploying MySQL Raft at Meta

We’re rolling out MySQL Raft with the aim to eventually replace our current MySQL semisynchronous databases.