Advancing Our Chef Infrastructure: Safety Without Disruption
Last year, I wrote a blog post titled Advancing Our Chef Infrastructure, where we explored the evolution of our Chef infrastructure over the years. We…
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Enabling Deep Model Explainability with Integrated Gradients at Uber
Uber’s ML platform Michelangelo now supports Integrated Gradients, enabling scalable, interpretable deep model explainability across TensorFlow™ and PyTorch™. Learn how this powers trust, debugging, and…
Scaling Privacy Infrastructure for GenAI Product Innovation
How does Meta empower its product teams to harness GenAI’s power responsibly? In this post, we delve into how Meta addresses the challenges of safeguarding…
Introducing CLI Agent Orchestrator: Transforming Developer CLI Tools into a Multi-Agent Powerhouse
Today we are introducing CLI Agent Orchestrator (CAO, pronounced “kay-oh”), an open source, multi-agent orchestration framework that transforms how developers work with AI-powered CLI tools…
Requirement Adherence: Boosting Data Labeling Quality Using LLMs
Harnessing the power of LLMs, Uber AI Solutions developed a system to reduce data labeling audits by 80%. Learn how the system detects labeling errors,…
Disaggregated Scheduled Fabric: Scaling Meta’s AI Journey
Disaggregated Schedule Fabric (DSF) is Meta’s next-generation network fabric technology for AI training networks that addresses the challenges of existing Clos-based networks. We’re sharing the…
Scaling LLM Inference: Innovations in Tensor Parallelism, Context Parallelism, and Expert Parallelism
At Meta, we are constantly pushing the boundaries of LLM inference systems to power applications such as the Meta AI App. We’re sharing how we…
Branching in a Sapling Monorepo
Sapling is a scalable, user-friendly, and open-source source control system that powers Meta’s monorepo. As discussed at the GitMerge 2024 conference session on branching, designing…
10X Backbone: How Meta Is Scaling Backbone Connectivity for AI
We’re sharing details on our journey to scale Meta’s Backbone network to support the increasing demands of new and existing AI workloads. We’ve developed new…
Rebuilding Uber’s Apache Pinot™ Query Architecture
The next chapter of real-time analytics at Uber. Uncover how Uber restructured its Apache Pinot™ query architecture to unlock a ton of new features, redefining…
Design for Sustainability: New Design Principles for Reducing IT Hardware Emissions
We’re presenting Design for Sustainability, a set of technical design principles for new designs of IT hardware to reduce emissions and cost through reuse, extending…
How Meta Is Leveraging AI To Improve the Quality of Scope 3 Emission Estimates for IT Hardware
As we focus on our goal of achieving net zero emissions in 2030, we also aim to create a common taxonomy for the entire industry…
OCP Summit 2025: The Open Future of Networking Hardware for AI
At Open Compute Project Summit (OCP) 2025, we’re sharing details about the direction of next-generation network fabrics for our AI training clusters. We’ve expanded our…
From Single-Node to Multi-GPU Clusters: How Discord Made Distributed Compute Easy for ML Engineers
From manual GPU configs to one-command clusters: Join Serrana Aguirregaray and Nathaniel Jenkins as they tell the story of Discord’s journey to build a developer-friendly…
During October, Treat a Friend to Nitro and Trick Out Your Profile for Halloween 🎃
Do you embrace tricks in the Shop or turn them into treats? Trick out your profile with shockingly sweet decorations, or treat your friend to…