In-house AI R&D: Nebius’ secret ingredient for truly AI‑centric cloud

Severe GPU scarcity and MLOps challenges are causing machine learning (ML) engineers to shift focus from model development to addressing infrastructure issues, which affects their productivity. To bridge this gap, a platform combines hardware, software, and ML proficiency, exemplified by building a scalable 10K GPU cluster and leveraging Nebius software for orchestrated machines and tools. An AI R&D team, led by Boris Yangel, utilizes a comprehensive ML platform for in-house large-scale distributed training to understand ML engineers' needs, acting as the first 'filter' to improve the platform before public launch. This team engages in dogfooding, testing infrastructure and features internally before release, enhancing resilience and efficiency through advancements like a flexible training framework, a robust data processing system, and cluster monitoring for high training goodput. The platform's open-source nature allows external clients to benefit from these innovations without replicating the AI R&D team's path, advancing a fully AI-centric cloud platform.

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