Azure Machine Learning Studio offers multiple ways to use your data to create ML models. Using Azure ML Designer to create a model. The Designer is the quickest way to start with custom machine ...

Microsoft announced the Azure Machine Learning studio web experience is generally available. The company yesterday (July 8) announced the advancement to GA, with a bevy of new features touching upon ...

The Azure Machine Learning Studio was introduced way back in 2014. It helps developers build and train custom models, and then deploy and manage them to the cloud or edge, while monitoring performance ...

When you create a new experiment in Azure Machine Learning Studio, you can start from scratch or choose from about 70 Microsoft samples, which altogether cover using most of the common models.

On the Azure Machine Learning side, Designer lets users visually connect datasets and modules on an interactive canvas to build, test, and deploy machine learning models. Designer supports ...

Azure Machine Learning automates machine learning to make it easier to build, train and deploy models. The service is generally available now, with pricing to go into effect February 1, 2019. BT ...

Matt Winkler delivered a talk at Microsoft Build 2018 explaining what is new in Azure Machine Learning. The new improvements come in several areas: making development easier, single container deployme ...

Understand, protect and control your machine learning solution. Over the past several years, machine learning has moved out of research labs and into the mainstream, and has transformed from a niche ...

Office can now suggest better phrases in Word or entire replies in Outlook, design your PowerPoint slides, and coach you on presenting them. Microsoft built those features with Azure Machine ...

Already present in Azure Databricks, a fully managed version of MLflow will be added to Azure Machine Learning and made available soon. Written by Andrew Brust, Contributor April 24, 2019 at 9:00 ...

The Azure Machine Learning Studio was introduced way back in 2014. The new web offering helps developers build and train custom models, and then deploy and manage them to the cloud or edge, while ...