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Scaling AI for an Immersive 3D Platform with 77 Million Daily Active Users

Posted Sep 13, 2024 | Views 41
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Maria Kazandjieva
Co-founder @ Graft

Maria is a co-founder and an engineering leader at Graft, an early-stage AI startup. Prior to that, Maria worked at Netflix, where her team earned two Emmy awards for technical achievement. She holds a PhD in Computer Science from Stanford University. Outside of work, you can find Maria kickboxing & trail running, baking & eating carbs, or relaxing with a non-fiction book and her two feline supurrvisors, Foosball and Gemma.

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Anupam Singh
VP, AI & Growth Engineering @ Roblox

Anupam leads Roblox's AI & Growth engineering teams, which provide the infrastructure for high throughput AI services for safety, recommendations, and assistants. Before Roblox, Anupam was chief customer officer at Cloudera, where he led product, engineering, and field teams for Data Warehousing products. Anupam has co-founded two companies in the Big Data space, acquired by Cloudera and Marketshare, respectively. Anupam built his database expertise on the SQL Query Optimizer teams at Oracle, Sybase (now SAP), and Informix (now IBM). He graduated from Pune University in India and holds patents in the areas of automatic SQL performance tuning, object databases, and resilient query execution.

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SUMMARY

Roblox’s infrastructure currently supports nearly 200 different ML models that run in real-time, 24/7 around the world — empowering creators to build immersive 3D experiences, keeping our users safe, and helping users discover amazing experiences. Powering AI at this scale requires a massive amount of computational power, novel problem-solving, tremendous cross-team coordination, and cost-effective planning. In this fireside chat, Anupam Singh (VP of AI & Growth Engineering @ Roblox) and Maria Kazandjieva (Co-founder @ Graft) will talk about Roblox’s journey to scale AI infrastructure, tools, and features to millions of daily active users. He will also share actionable insights on how engineering leaders can most effectively:

Decide when to run inference on CPUs vs GPUs Scale inference to more than 30,000 queries per second Build a stack that is portable between private and public clouds Use open source extensively to benefit from the entire community Progress from first demo to a scalable product offering

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