Leverage the power of machine learning on windows
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The document outlines the significance of machine learning (ML) and artificial intelligence (AI) for developers, emphasizing its ability to solve complex problems traditionally tackled by standard algorithms. It highlights key tools and services available through Azure for easy implementation of ML, including Azure Cognitive Services, ML.NET, and automated machine learning (AutoML) functionalities. The content also covers the benefits of using these technologies, such as cost savings, improved customer interactions, and enhanced model development efficiency.
Leverage the power of machine learning on windows
- 1. #insiderDevTour Leverage the Power of Machine Learning on Windows Mia Chang
- 2. #insiderDevTour Why AI and ML? How can you get started? What else? What we'll talk about today
- 3. #insiderDevTour As a developer, why should you care about AI and ML?
- 4. Examples Classify if a customer is a retention risk Identify objects in images and videos Detect defects in an industrial process Some problems are difficult to solve using traditional algorithms and procedural programming. These examples are good candidates for machine learning.
- 5. When can you use Machine Learning? A repetitive decision or process Solution lacks an explicit definition A lot of training data is available
- 6. #insiderDevTour 90% Reduced time from documents to insights $300K Cost savings per day 40x More insurance cases reviewed 200K Customers interacting with chatbots +20% Increase in customer satisfaction +32% Increase in sales
- 7. #insiderDevTour How can you get started with AI and ML?
- 8. #insiderDevTour Prepare Your Data Quickly launch and scale Spark on demand Rich interactive workspace and notebooks Seamless integration with all Azure data services Build and Train Broad frameworks and tools support TensorFlow, Cognitive Toolkit, Caffe2, Keras, MxNET, PyTorch Deploy Docker containers Windows AI Platform Azure Machine Learning Machine Learning Landscape
- 9. #insiderDevTour Machine Learning for Developers Enable Multi-Input Azure Cognitive Services Ink Recognizer Increase Functionality Windows AI platform Sentiment Analysis ML.NET Price prediction
- 10. #insiderDevTour Machine Learning for Developers Enable Multi-Input Azure Cognitive Services Ink Recognizer Increase Functionality Windows AI platform Sentiment Analysis ML.NET Price prediction
- 11. #insiderDevTour
- 12. Azure Cognitive Services The most comprehensive pre-trained AI Language Vision Speech Decision Web search Bing Spell Check Custom Vision Personalizer Form Recognizer Neural Text-to-Speech Anomaly Detector Content Moderator Content Moderator Custom Speech Speech transcription Text-to-Speech Conversation transcription capability Face Video Indexer Ink Recognizer Computer Vision Language Understanding QnA Maker Text Analytics Translator Text Bing Web Search Bing Custom Search Bing Video Search Bing Image Search Bing Local Business Search Bing Visual Search Bing Entity Search Bing News Search Bing Autosuggest
- 13. Azure Cognitive Services The most comprehensive pre-trained AI Ink Recognizer
- 14. Azure Cognitive Services //build/ Ink Recognizer aka.ms/idt2019resources
- 16. #insiderDevTour Demo Notes: Demo Name <Script for the demo. Use more than one slide if you need to>
- 17. #insiderDevTour Machine Learning for Developers Enable Multi-Input Azure Cognitive Services Ink Recognizer Increase Functionality Windows AI platform Sentiment Analysis ML.NET Price prediction
- 18. #insiderDevTour DESKTOP CLOUDWEB MOBILE ML .NET Your platform for building anything IoTGAMING
- 19. #insiderDevTour ML.NET Machine Learning framework for building custom ML Models Proven at scale Azure, Office, Windows Extensible TensorFlow, ONNX and Infer.NET Cross-platform and open-source Runs everywhere Easy to use tools CLI + UI-based tool for building models
- 20. #insiderDevTour ML.NET Tooling ML.NET CLI global tool accelerates productivity AutoML Model Builder
- 21. #insiderDevTour How much is the taxi fare for 1 passenger going from Cape Town to Johannesburg? AutoML with ML.NET ML.NET CLI global tool accelerates productivity
- 22. #insiderDevTour Criterion Loss Min Samples Split Min Samples Leaf XYZ Parameter 1 Parameter 2 Parameter 3 Parameter 4 … Distance Trip time Car type Passengers Time of day … Gradient Boosted Nearest Neighbors SGD Bayesian Regression LGBM … Distance Gradient Boosted 30% Model Car type Passengers Getting started w/machine learning can be hard ML.NET takes the guess work out of data prep, feature selection & hyperparameter tuning Which algorithm? Which parameters?Which features?
- 23. #insiderDevTour N Neighbors Weights Metric P ZYX Which algorithm? Which parameters?Which features? Distance Trip time Car type Passengers Time of day … Gradient Boosted Nearest Neighbors SGD Bayesian Regression LGBM … Nearest Neighbors Criterion Loss Min Samples Split Min Samples Leaf XYZ 50% Model Iterate 30% Gradient BoostedDistance Car brand Year of make Car type Passengers Trip time Getting started w/machine learning can be hard ML.NET takes the guess work out of data prep, feature selection & hyperparameter tuning
- 24. #insiderDevTour 25%40%70% 25% 95% 25% 25% 25% 25% 40% 40% 40% 40% 70% 70% 70%Enter data Define goals Apply constraints Input Intelligently test multiple models in parallel Optimized model ML.NET accelerates model development
- 25. #insiderDevTour 70%95% Feature importance Distance Trip time Car type Passengers Time of day 0 1 Model B (70%) Distance 0 1 Trip time Car type Passengers Time of day Feature importance Model A (95%) ML.NET accelerates model development with model explainability
- 27. #insiderDevTour # STEP 1: Load data IDataView trainingDataView = mlContext.Data.LoadFromTextFile<TaxiTrip>( ... ) IDataView testDataView = mlContext.Data.LoadFromTextFile<TaxiTrip>( ... ) # Display first few rows of the training data ConsoleHelper.ShowDataViewInConsole(mlContext, trainingDataView) # STEP 2: Initialize user-defined progress handler that AutoML will invoke after each model var progressHandler = new RegressionExperimentProgressHandler() # STEP 3: Run AutoML regression experiment ExperimentResult<RegressionMetrics> experimentResult = mlContext.Auto() .CreateRegressionExperiment(ExperimentTime) .Execute(trainingDataView, LabelColumnName, progressHandler: progressHandler) # Print top models found by AutoML PrintTopModels(experimentResult) # STEP 4: Evaluate the model on test data RunDetail<RegressionMetrics> best = experimentResult.BestRun ITransformer trainedModel = best.Model # Run best model on test data IDataView predictions = trainedModel.Transform(testDataView) # STEP 5: Save trained model to a .ZIP file mlContext.Model.Save(trainedModel, trainingDataView.Schema, ModelPath)
- 28. #insiderDevTour Machine Learning for Developers Enable Multi-Input Azure Cognitive Services Ink Recognizer Increase Functionality Windows AI platform Sentiment Analysis ML.NET Price prediction aka.ms/idt2019resources
- 29. #insiderDevTour WinML Practical, simple model-based API for ML inferencing on Windows DirectML Realtime, high control ML operator API; part of DirectX family Compute Driver Model Robust hardware reach/abstraction layer for compute and graphics silicon DirectML API DirectX12 WinML API ONNX Runtime Compute Driver Model GPU VPU xPU CPU Windows AI platform
- 31. #insiderDevTour # Load a model var modelFile = await StorageFile.GetFileFromApplicationUriAsync(new Uri(modelPath)) LearningModel model = wait LearningModel.LoadFromStorageFileAsync(modelFile) # Bind a model LearningModelBinding binding = new LearningModelBinding(session) ImageFeatureValue image = ImageFeatureValue.CreateFromVideoFrame(inputFrame) binding.Bind(inputName, image); # Evaluate var result = await session.EvaluateAsync(binding, "0")
- 32. #insiderDevTour ✔ Azure Custom Vision makes it easy to create an image model ✔ WinMLTools converts existing models from TensorFlow, Keras, CoreML, scikit-learn, LIBSVM and XGBoost ✔ Azure Machine Learning Service provides an end-to-end solution for preparing your data and training your model ✔ ML.NET Getting an ONNX Model
- 33. #insiderDevTour WinML Benefits • Low latency • Cost Effectiveness • Flexibility DirectML API DirectX12 WinML API ONNX Runtime Compute Driver Model GPU VPU xPU CPU Windows AI platform
- 34. Azure AI Ink Recognizer Windows AI platform WinML ML.NET AutoML What we’ve seen today… …and two exciting tools announced at Build DirectML Vision Skills
- 35. ✔ Powers Windows ML hardware acceleration ✔ Delivers broad hardware support through DirectX12 ✔ Built for real-time performance ✔ Raytracing DirectML API DirectX12 WinML API ONNX Runtime Compute driver model GPU VPU xPU CPU What is DirectML?
- 36. Object detector Skeletal detector Sentiment analyzer Vision Skills
- 37. #insiderDevTour Why AI and ML? ✔ The future of Apps Azure Cognitive Services ✔ Comprehensive pre-trained AI ✔ Many new updates! ML.NET ✔ AutoML ✔ Model Builder The Windows AI platform ✔ WinML ✔ DirectML ✔ Vision Skills Recap
- 38. #insiderDevTour Applications Today The Future of Applications AI ML The Power of Machine Learning
- 39. #insiderDevTour Learn more! Check out the labs and resources! aka.ms/insiderdevtour-labs