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Getting Started with Visual Studio Tools for AI

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Getting Started with Visual Studio Tools for AI

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Getting Started with Visual Studio Tools for AI

  1. 1. Integrated w/Azure Machine Learning Integrated w/Cognitive Services Create new deep learning projects easily Scale Out with Azure Batch AI Generate C# code from TensorFlow & ONNX models Convert models to ONNX Monitor model training progress & GPU utilization Visualize your model training with TensorBoard Get started quickly with the Samples Gallery Visual Studio Tools for AI AI developer tool to train models & infuse AI into your apps http://aka.ms/vstoolsforai http://aka.ms/vscodetoolsforai
  2. 2. Data Scientists Developers
  3. 3. AIlab.microsoft.com http://aka.ms/styletransfer
  4. 4. Creating Art with Image Style Transfer Models Data Sources Ingest / Prepare Model Train with Cloud AI Deploy Consume AC TION INTELLIGENC EDATA Azure Blob Raw storage Azure Machine Learning Docker Image + IoT Hub Model Update + Manageability 10 01 Model: VGG-19 Code: Tensorflow and Keras Microsoft Common Objects in Context (COCO) 328k images 91 different types of objects could be recognized by a 4 year old Visual Studio Tools for AI Deep Learning Virtual Machine (DLVM)
  5. 5. • We want to preserve stylistic features but not spatial structure • Loss functions measure high-level perceptual and semantic differences between images • Use pretrained loss network called VGG-19 trained on the ImageNet dataset Creating Art with Image Style Transfer Models Inspired by “Perceptual Losses for Real-Time Style Transfer and Super-Resolution” https://arxiv.org/abs/1603.08155 Approach (see paper for more detail) What’s happening inside
  6. 6. Create new deep learning projects easily Use TensorFlow, CNTK, Keras, Caffe2, Chainer, and more. Debug and iterate quickly with the power of Visual Studio.
  7. 7. Integrated with Azure Machine Learning Get started quickly with the sample gallery. Manage your experiments and models. Deploy in the cloud or on the edge.
  8. 8. Scale Out with Azure Batch AI Elastically scale training in Azure. Select Docker container, # VMs per job. Pay only for what you use when jobs are running.
  9. 9. Monitor model training progress & GPU utilization Integrated with TensorBoard to easily monitor & visualize your model training. GPU heatmap provides visibility for optimizing your resource utilization
  10. 10. Storage Browser to upload data, copy model & view logs Easily upload data to remote machines, download model files and view logs Working in the cloud is as convenient as your desktop.
  11. 11. Infuse AI into your apps today Include model in your app like any other resource … Or deploy to Azure ML and call via REST API
  12. 12. Bringing the best of AI to Azure and the best of Azure to AI Pre-Built AI Azure Cognitive Services Conversational AI Azure Bot Service Custom AI Azure Machine Learning
  13. 13. Integrated w/Azure Machine Learning Integrated w/Cognitive Services Create new deep learning projects easily Scale Out with Azure Batch AI Generate C# code from TensorFlow & ONNX models Convert models to ONNX Monitor model training progress & GPU utilization Visualize your model training with TensorBoard Get started quickly with the Samples Gallery Visual Studio Tools for AI AI developer tool to train models & infuse AI into your apps http://aka.ms/vstoolsforai http://aka.ms/vscodetoolsforai
  14. 14. find out more!  Learn more about  Cognitive Services – https://aka.ms/cogsvcs  Azure ML – https://aka.ms/azuremlbuild  Visual Studio Tools for AI - https://aka.ms/vstoolsforai  Deep Learning VMs - https://aka.ms/dlvmbuild  Batch AI – https://aka.ms/batchaibuild  ML.Net - https://docs.microsoft.com/en-us/dotnet/machine-learning/

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