Webinar GLUGNet - Machine Learning.Net and Windows Machine Learning
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The document provides an overview of getting started with machine learning using ML.NET and Windows Machine Learning (WinML). It discusses the importance of AI and ML for developers, outlines various machine learning tasks such as classification and regression, and describes tools and services available through Azure for model training and deployment. Additionally, it highlights the ease of use and flexibility of ML.NET for building custom machine learning models across different platforms.
![Getting Started with
Machine Learning .Net and
Windows Machine Learning
[ ML.Net & WinML ]
Bruno Capuano
Innovation Lead @Avanade
@elbruno | http://elbruno.com](https://image.slidesharecdn.com/20190220glugmlnet05-slideshare-190222011539/85/Webinar-GLUGNet-Machine-Learning-Net-and-Windows-Machine-Learning-1-320.jpg)
Webinar GLUGNet - Machine Learning.Net and Windows Machine Learning
- 1. Getting Started with Machine Learning .Net and Windows Machine Learning [ ML.Net & WinML ] Bruno Capuano Innovation Lead @Avanade @elbruno | http://elbruno.com
- 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. IBM slaps patent on coffee-delivering drones that can read your MIND (link)
- 5. IBM slaps patent on coffee-delivering drones that can read your MIND (link)
- 6. IBM coffee-delivering drones test footage
- 7. “It has exquisite buttons … with long sleeves …works for casual as well as business settings”{f(x) {f(x) Machine Learning: “Programming the Unprogrammable”
- 8. f(x) Model Machine Learning creates a Using this data Machine Learning: “Programming the UnProgrammable”
- 9. Is this A or B? How much? How many? How is this organized? Regression ClusteringClassification Machine Learning Tasks
- 10. Get started with Machine Learning 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 Windows Machine Learning
- 12. Windows ML uses ONNX models
- 13. 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?
- 14. 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
- 16. DESKTOP CLOUDWEB MOBILE ML .NET Your platform for building anything IoTGAMING
- 17. Easy / Less Control Full Control / Harder Vision Speech Language Knowledge SearchLabs TextAnalyticsAPI client = new TextAnalyticsAPI(); client.AzureRegion = AzureRegions.Westus; client.SubscriptionKey = "1bf33391DeadFish"; client.Sentiment( new MultiLanguageBatchInput( new List<MultiLanguageInput>() { new MultiLanguageInput("en","0", "This vacuum cleaner sucks so much dirt") })); e.g. Sentiment Analysis using Azure Cognitive Services 9% positive Pre-built ML Models (Azure Cognitive Services)
- 18. ML.NET is for building custom models Custom models Easier / Less Control Harder / Full Control Pre-built models TensorFlow ML.NETVisionSpeech LanguageKnowledge Search
- 19. Prepare Your Data Build & Train Run Build your own custom machine learning models
- 20. Artificial Intelligence: Image Analysis
- 22. Is this A or B? Kid or Baby Based on the age: Kid or Baby Age classes explained
- 23. And more! Samples @ https://github.com/dotnet/machinelearning-samples Customer segmentation Recommendations Predictive maintenance Forecasting Issue Classification Image classification Object detection Sentiment Analysis A few things you can do with ML.NET …
- 24. Proven & Extensible Open Source https://github.com/dotnet/machinelearning Build your own Supported on Windows, Linux, and macOS Developer Focused ML.NET 0.10.0 (Preview) Machine Learning framework made for .NET developers
- 25. Windows 10 (Windows Defender) Power Point (Design Ideas) Excel (Chart Recommendations) Bing Ads (Ad Predictions) + moreAzure Stream Analytics (Anomaly Detection) ML.NET is Proven at scale, enterprise ready
- 26. ML.NET is a framework for building custom ML Models
- 27. Machine Learning.Net How to use ML.Net
- 28. Less Control / Easy Existing Solutions Build your own (custom) ML Models
- 29. ML.Net Working with 2 or more columns MakeMagicHappen(); https://www.microsoft.com/net/learn/apps/machi ne-learning-and-ai
- 30. Load Data Extract Features Model Consumption Train Model Evaluate Model Prepare Your Data Build & Train Run Machine learning workflow
- 31. Load Data Extract Features Train Model Evaluate Model Model consumption labels + plain text labels + feature vectors model End to End ML Workflow
- 32. Load Data Extract Features Train Model Evaluate Model Model consumption labels + plain text labels + feature vectors Enter... in ML.NETLearningPipelines! model End to End ML Workflow
- 33. Load Data Extract Features Train Model Evaluate Model Model consumption Machine Learning is Iterative
- 35. ML.Net GitHub Issue Automatic Label MakeMagicHappen(); https://github.com/elbruno
- 36. ML.Net, working with TensorFlow frozen models MakeMagicHappen(); https://www.microsoft.com/net/learn/apps/machi ne-learning-and-ai
- 37. • API improvements • Additional ML Tasks and Scenarios • Improved Deep Learning with TensorFlow • Scale-out on Azure • Better GUI to simplify ML tasks • Improved tooling in Visual Studio • Improvements for F# • Language Innovation for .NET Road Ahead for ML.NET
- 38. Bruno Capuano Innovation Lead @Avanade @elbruno | http://elbruno.com Q&A Thanks!