This capability allows customers to build models with Azure Machine Learning anywhere, including on-premises, multi-cloud environments, and at the edge.

Microsoft Azure’s plush, Python-based environment covers the full machine learning and deep learning development cycle ...

The COVID-19 pandemic has accelerated the business need for remote collaboration in the world of software development. Multiple disciplines are coming together to create novel solutions to complex ...

While Microsoft's Azure DevOps team has been busy lately, introducing Scalar to speed up Git operations and other initiatives, much more work is planned for the cloud-based successor to Visual Studio ...

Microsoft is working to bring open source machine learning models into Azure applications and services.

To spot faults quickly even if they take a month to show up, Azure feeds signals into a machine learning system: in the future, you will be able to do that for your own cloud workloads.

Additionally, users can leverage Azure DevOps or GitHub Actions to schedule, manage and automate their machine learning pipelines and perform advanced data-drift analysis to improve a model's ...

This remarkable achievement showcases their expertise in enabling customer adoption of Azure AI solutions and help customers establish secure software development practices through DevOps principles.

Microsoft is joining the Databricks-backed MLflow project for machine learning experiment management. Already present in Azure Databricks, a fully managed version of MLflow will be added to Azure ...