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Data, Systems and Networking

Live Q&A Session #2

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Data, Systems and Networking

Live Q&A Session #1

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Data, Systems and Networking

Gang Scheduling for Llama

The rapid advancement of AI has necessitated a fundamental shift in infrastructure, moving from homogenous workloads that fit within a single server to multi-host workloads requiring tight container coordination across multiple servers. This talk explores the motivations and design principles behind this shift, focusing on the implementation of first-class support for gang scheduling at all […]
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Data, Systems and Networking

Product Reliability in Google Maps

While our organization excelled at maintaining server SLOs for Google Maps, we discovered that many user-impacting incidents, particularly those stemming from client-side issues like mobile app rollouts, remained undetected by server-centric monitoring. This realization prompted a strategic shift towards product reliability, prioritizing the end-user experience. This talk will discuss how we navigated this transition, sharing […]
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Data, Systems and Networking

Journey to 1000 Models: Scaling Instagram’s algorithm without the Reliability Nightmare

At the beginning of 2023, Instagram had O(10) gpu models, a manual release process, and a manual monitoring setup. This talk will be centered around our journey to 1000 models: the bumps along the road and the foundational work built to make monitoring model health faster and more accurate. We’ll be going over model registry, […]
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Data, Systems and Networking

Building KVStore for ML Workloads at Pinterest

This presentation introduces Pinterest’s KVStore, a distributed key-value store designed to support machine learning workloads that are central to Pinterest’s functions. KVStore enables efficient low-latency ML feature serving with various data update methods. KVStore is crucial for Pinterest’s AI/ML-driven platform, evolving to meet business needs with a focus on reliability and efficiency at scale.
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Data, Systems and Networking

Splitting the Monolith

For over a decade, Facebook and most other Meta products have been powered by a single monolithic PHP application. Since 2022, we have been investing in a multitenancy framework to allow product specialization with less operational overhead. This has allowed Meta to move fast in generative AI, by providing a familiar development environment for our […]
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Data, Systems and Networking

Turbocharging AI/ML workloads: Revving Up Speed and Resilience

Race cars are built for speed and resilience, equipped with cutting-edge features to reach high velocities while maintaining a firm grip on the perilous track. What if we could apply similar features to boost the speed and resilience of AI/ML jobs running over complex networking fabrics? In this session, we’ll dive into the key networking […]
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Data, Systems and Networking

How We’re Scaling Discovery at Netflix Reliably

In the dynamic realm of streaming services, reliability and scalability are imperatives. This talk unveils the sophisticated architecture of Netflix’s Member Discovery System, known as Mosaic, which powers key member-facing pages. Discover the innovative strategies that ensure Mosaic’s robustness and reliability, and learn how Netflix sets the standard in delivering a seamless user experience to […]
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Data, Systems and Networking

AI Hardware Reliability at Scale

This talk will describe our journey with AI hardware reliability (GPU/Silicon) running large scale training and inference in Meta. It will highlight our efforts across the ecosystem, covering vendor systems and our own custom silicon efforts to run AI hardware reliably at scale. For SW/Services audience, this will provide a under-the-hood look into how AI […]
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Data, Systems and Networking

Experience Operating Large GPU Clusters at Organizational Scale

We outline Nvidia’s experience managing a large-scale internal GPU compute platform spanning multiple heterogeneous clusters. The platform supports thousands of users and hundreds of project accounts, handling a diverse mix of training and batch inference workloads across various research fields. We focus on three key challenges: researcher productivity, resource utilization, and operational efficiency. To improve […]
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Data, Systems and Networking

Model Freshness and Its Infra Implications

Meta’s recommendation systems rely on “freshness” – the speed at which user interaction signals are ingested, trained, and utilized. To improve model freshness, Meta developed solutions addressing scaling, serving footprints, and diverse architectures.
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