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Scale Python AI workloads from a single notebook to thousands of CPUs/GPUs. In #AzureFriday, Scott Hanselman 🌮 talks with Omar Shorbaji about building, training, and serving AI apps on Anyscale on Azure, powered by Ray, and running on AKS (without managing Kubernetes). Watch:

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Microsoft Azure

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16,550 просмотров • 16 дней назад •via X (Twitter)

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Now available: Sora in Azure OpenAI in Azure AI Foundry. This update empowers teams to: 🎨 Seamlessly integrate multimodal AI into apps and workflows via API-first design 🎨 Scale securely with built-in governance, identity, and billing Read more:

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AI in the network is shifting the conversation beyond GPUs. At MWC 2026, Intel's Kevork Kechichian, EVP & GM of the Data Center Group, explains why inference workloads are bringing CPUs back into focus, especially in telco environments where power, space, and efficiency matter. With platforms like Intel Xeon and Clearwater Forest, operators are seeing up to 30% performance gains and 60% better power efficiency, helping scale AI workloads without overhauling infrastructure. As networks evolve toward AI-driven operations and 6G, one principle is becoming clear: The future of AI infrastructure is about using the right compute for the right workload.

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Our approach to AI infra is simple: build the most fungible and flexible fleet to meet the real world's needs across inference and training as Scott Guthrie shared with Alex Kantrowitz. And we are already doing it at scale today, as we power the biggest AI workloads like Copilot and ChatGPT, APIs that power 3P products & enterprise workloads and high scale training.

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Announcing Cloudy, a platform that seamlessly handles your training infrastructure. You can rent a single H100 or a cluster of 1000 H100s, manage petabyte-scale storage volumes & seamlessly go from running experiments to managing large scale training runs on a single interface.

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“Any model, any agent, any accelerator, and any cloud.” Red Hat’s Brian Stevens explains why open AI infrastructure matters as enterprises scale agentic AI workloads, and how Red Hat and AMD are collaborating to help simplify deployment with AMD EPYC processors and AMD Instinct GPUs. Watch the full video on YouTube:

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Had super cool discussion with Sam Altman on our strategy of creating the entire AI stack - GPUs, model, and apps. Willing to collaborate with India on all three.

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