Azure Machine Learning (AML) team is excited to announce the availability of Azure Arc-enabled Machine Learning (ML) public preview release. All customers of Azure Arc-enabled Kubernetes now can deploy AzureML extension release and bring AML to, and the edge using Kubernetes on their hardware of choice. The design for Azure Arc-enabled ML helps IT Operators leverage native Kubernetes concepts such as namespace, node selector, and resources requests/limits for ML compute utilization and optimization. By letting the IT operator manage ML compute setup, Azure Arc-enabled ML creates a seamless AML experience for data scientists who do not need to learn or use Kubernetes directly. Data scientists now can focus on models and work with tools such as Azure Machine Learning AML Studio, AML 2.0 CLI, AML Python SDK, productivity tools like Jupyter notebook, and ML frameworks like TensorFlow and PyTorch.

Azure Arc is Microsoft's offering for allowing customers to bring Azure services and management to any infrastructure, including AWS and Google Cloud. This year, during the virtual Ignite conference, the company announced the preview of Azure Arc-enabled machine learning, which extends Azure machine learning capabilities to hybrid and multi-cloud environments. Microsoft launched Azure Arc in November 2019 at their Ignite conference, and the service received support for Kubernetes - announced during the Build conference 2020. Furthermore, the company brought more capabilities to Azure Arc, which they announced at Ignite 2020 with Azure Arc enabled data services. And now, at this year's Ignite, Microsoft continues adding capabilities to the service with Arc-enabled machine learning.