Scaling AI/ML with Azure and Anyscale: Unlocking Business Potential

Key points

Microsoft and Anyscale partnership: Bringing Anyscale’s managed Ray service to Azure as a first-party offering in private preview, making it easier to scale AI/ML workloads.
Ray and Anyscale on Azure: Combining the open-source distributed computing framework with Anyscale’s managed platform and Azure’s enterprise-grade Kubernetes infrastructure for efficient and scalable AI/ML workloads.
Simplified AI/ML development: Enabling developers to focus on model performance and innovation, rather than infrastructure complexity, with Ray’s Pythonic APIs and Anyscale’s managed experience.

According to sources, a new partnership between Microsoft and Anyscale is set to revolutionize the way AI/ML workloads are scaled on Azure. The partnership brings Anyscale’s managed Ray service to Azure as a first-party offering in private preview, making it easier for developers to scale their AI/ML workloads without the complexity of building and managing infrastructure themselves.

Ray, the open-source distributed computing framework, was born at UC Berkeley’s RISELab and is now coming to Azure in a whole new way. Ray makes it simple for developers to scale code from a single laptop to a large cluster with minimal changes, using Pythonic APIs that allow functions and classes to be transformed into distributed tasks and actors without altering core logic.

Anyscale, the company founded by Ray’s creators, is bringing its managed Ray service to Azure, delivering the simplicity of Anyscale’s developer experience on top of Azure’s enterprise-grade Kubernetes infrastructure. This new managed service will make it possible to run distributed Python workloads with native integrations, unified governance, and streamlined operations, all inside an Azure subscription.

At the heart of this offering is RayTurbo, Anyscale’s high-performance runtime for Ray, designed to maximize cluster efficiency and accelerate Python workloads. RayTurbo enables teams on Azure to spin up Ray clusters in minutes, dynamically allocate tasks across CPUs, GPUs, and heterogeneous nodes, and run large experiments quickly and cost-effectively with elastic scaling, GPU packing, and native support for Azure spot VMs.

Azure Kubernetes Service (AKS) powers this new managed offering, providing the infrastructure foundation for running Ray at production scale. AKS handles the complexity of orchestrating distributed workloads while delivering the scalability, resilience, and governance that enterprise AI applications require.

With this partnership, Microsoft and Anyscale are bringing together the best of open-source Ray, managed cloud infrastructure, and Kubernetes orchestration. By pairing Ray’s distributed computing platform for Python with Anyscale’s management capabilities and AKS’s robust orchestration, Azure customers gain flexibility in how they can scale AI workloads. Whether you want to start small with rapid experimentation or run mission-critical systems at global scale, this offering gives you the choice to adopt distributed computing without the complexity of building and managing infrastructure yourself. Microsoft and Anyscale are removing operational barriers and giving developers more ways to innovate with Python on Azure, so they can move faster, scale smarter, and focus on delivering breakthroughs.