Ai on the edge... and containers
•
0 gostou•79 visualizações
"Ai on the edge... and containers" deck for GlobalAiBootcamp and GlobarMRBootcamp 2018
Recomendados
Recomendados
Mais conteúdo relacionado
Mais procurados
Mais procurados (20)
Semelhante a Ai on the edge... and containers
Semelhante a Ai on the edge... and containers (20)
Mais de Riccardo Cappello
Mais de Riccardo Cappello (11)
Último
Último (20)
Ai on the edge... and containers
- 1. AI on the Edge… and Containers Riccardo Cappello Microsoft MVP Microsoft Azure Intel Software Innovator Marco Dal Pino Microsoft MVP Windows Development Intel Software Innovator / Intel Blackbelt
- 2. why should I care about AI and ML? As a developer,
- 3. Some problems are difficult to solve using traditional algorithms and procedural programming.
- 4. AI solves hard problems Smart Ink • Classify strokes as text, shapes, freehand drawings • Classify text into words, paragraphs, lines, bullets • Extract entities: phone numbers, names, dates • Assign meanings: date references, known contacts
- 5. Prepare Data Build & Train Evaluate Azure Databricks Azure Machine Learning Quickly launch and scale Spark on demand Rich interactive workspace and notebooks Seamless integration with all Azure data services Broad frameworks and tools support: TensorFlow, Cognitive Toolkit, Caffe2, Keras, MxNET, PyTorch In the cloud – on the edge Docker containers Get started with machine learning Windows Machine Learning
- 6. ScalabilityLow latency Flexibility AI on the edge
- 7. Windows ML
- 8. 1. Developers can focus on their data and their scenarios, using Windows ML for model evaluation 2. Enables using ML models trained with a diverse set of toolkits 3. Hardware acceleration gets fast evaluation results across the diversity of the entire Windows device ecosystem. Windows ML solves three problems for you Direct3D GPU CPU DirectML Model Inference Engine WinML Win32 API WinML UWP API Win32 App WinML Runtime UWP App
- 9. Windows ML uses ONNX models
- 10. • Azure Machine Learning Services gives you an end-to-end solution to prepare data, and train your model in the Cloud. • WinMLTools converts existing models from CoreML, scikit-learn, LIBSVM, and XGBoost • Azure Custom Vision makes it easy to create your own image models - https://customvision.ai/ • Azure AI Gallery curates models for use with Windows ML - https://gallery.azure.ai/models How do I get ONNX models to use in my application?
- 12. More edges with Custom Vision
- 13. Custom Vision models targets CoreML iOS 11 & later TensorFlow Android ONNX Windows ML Dockerfile Azure IoT Edge, Azure Functions, AzureML
- 14. Custom Vision offline models with iOS & Android Demo
- 16. AI on Raspeberry Pi with Docker Demo
- 17. Modify files to make things work Dockerfile: add the following https://gist.github.com/marcominerva/ 74ab33bef3660bb9a0f6db2f6cebd97b
- 18. References • https://onnx.ai • https://gallery.azure.ai/models • Emoji8: https://www.microsoft.com/en- us/store/p/emoji8/9n5vj4hkp6x1 • https://customvision.ai • https://azure.microsoft.com/en-us/blog/running-cognitive- service-containers/ • https://github.com/DotNetToscana/CustomVisionCompanion
- 19. Thanks! Questions? Riccardo Cappello Microsoft MVP Microsoft Azure Intel Software Innovator Marco Dal Pino Microsoft MVP Windows Development Intel Software Innovator / Intel Blackbelt