Visual Studio Tools for AI

Build, test, and deploy deep learning and AI solutions
Download the Visual Studio Extension
Visual Studio Tools for AI

Develop, debug and deploy deep learning and AI solutions

Visual Studio Tools for AI is an extension that supports deep learning frameworks including Microsoft Cognitive Toolkit (CNTK), Google TensorFlow, Theano, Keras, Caffe2 and more. You can use additional deep learning frameworks via the open architecture. Visual Studio Tools for AI leverages existing code support for Python, C/C++/C#, and supplies additional support for Cognitive Toolkit BrainScript.

Get started quickly developing deep learning and AI solutions
Get started quickly with the Samples Gallery

Get started quickly with the Samples Gallery

Visual Studio Tools for AI is integrated with Azure Machine Learning to make it easy to browse through a gallery of sample experiments using CNTK, TensorFlow, MMLSpark and more. This makes it easy to get started with deep learning and AI projects quickly.

Scale out deep learning training and operationalize AI models in Azure

Visual Studio Tools for AI integrates with Azure Batch AI and Azure Machine Learning services to enable submitting deep learning jobs to Azure GPU VMs, Spark clusters and more. You can monitor the performance of your recent experiments and then generate a web service to power a new intelligent application.

Scale out deep learning training and operationalize AI models in Azure
Productive AI developer tools to train models and infuse AI into your apps

Productive AI developer tools to train models and infuse AI into your apps

Visual Studio Tools for AI enables developers and data scientists the most robust set of integrated tooling for creating, debugging, and deploying their custom deep learning models. Using the power of Visual Studio you can seamlessly build an app using the model you just trained without switching IDEs.

Visualize your model processing with integrated open tools like TensorBoard

Visual Studio Tools for AI also has integrated monitoring and visualization of model training and experimentation using TensorBoard. Open your jobs in TensorBoard for runs both locally and in remote VMs.

Visualize your model processing with integrated open tools like TensorBoard