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2018 11 14 Artificial Intelligence and Machine Learning in Azure

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2018 11 14 Artificial Intelligence and Machine Learning in Azure

  1. 1. Bruno Capuano Innovation Lead @Avanade @elbruno |
  2. 2. why should I care about AI and ML? As a developer,
  3. 3. Some problems are difficult to solve using traditional algorithms and procedural programming.
  4. 4. IBM slaps patent on coffee-delivering drones that can read your MIND (link)
  5. 5. IBM slaps patent on coffee-delivering drones that can read your MIND (link)
  6. 6. IBM coffee-delivering drones test footage
  7. 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. 8. f(x) Model Machine Learning creates a Using this data Machine Learning: “Programming the UnProgrammable”
  9. 9. Is this A or B? How much? How many? How is this organized? Regression ClusteringClassification Machine Learning Tasks
  10. 10. 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 Get started with machine learning
  11. 11. MakeMagicHappen();
  12. 12. 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 - Azure AI Gallery curates models for use with Windows ML - How do I get ONNX models to use in my application?
  13. 13. Microsoft AI Platform
  14. 14. Microsoft AI platform Azure AI Services Azure Infrastructure Tools
  15. 15. { "tags":[ "train", "platform", "station", "building", "indoor", "subway", "track", "walking", "waiting", "pulling", "board", "people", "man", "luggage", "standing", "holding", "large", "woman", "yellow", "suitcase" ], "captions":[ { "text":"people waiting at a train station", "confidence":0.833099365 } ] } [ { "name":"train", "confidence":0.9975446 }, { "name":"platform", "confidence":0.995543063 }, { "name":"station", "confidence":0.9798007 }, { "name":"indoor", "confidence":0.927719653 }, { "name":"subway", "confidence":0.838939846 }, { "name":"pulling", "confidence":0.431715637 } ] Computer Vision API
  16. 16. Cognitive Services MakeMagicHappen();
  17. 17. Azure Machine Learning Studio
  18. 18. 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)
  19. 19. Platform for emerging data scientists to graphically build and deploy experiments • Rapid experiment composition • > 100 easily configured modules for data prep, training, evaluation • Extensibility through R & Python • Serverless training and deployment Some numbers: • 100’s of thousands of deployed models serving billions of requests Azure Machine Learning Studio
  20. 20. Deploy the Model Score and Evaluate the Model Model the Data Transform the Data Clean the Data Get the Data Machine Learning Steps
  21. 21. Confusion Matrix Truth true false Guess positive 𝑡𝑟𝑢𝑒 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒 𝑓𝑎𝑙𝑠𝑒 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒 𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 = 𝑡𝑝 𝑡𝑝 + 𝑓𝑝 negative 𝑓𝑎𝑙𝑠𝑒 𝑛𝑒𝑔𝑎𝑡𝑖𝑣𝑒 𝑡𝑟𝑢𝑒 𝑛𝑒𝑔𝑎𝑡𝑖𝑣𝑒 𝑟𝑒𝑐𝑎𝑙𝑙 = 𝑡𝑝 𝑡𝑝 + 𝑓𝑛 𝑎𝑐𝑐𝑢𝑟𝑎𝑐𝑦 = 𝑡𝑝 + 𝑡𝑛 𝑡𝑝 + 𝑡𝑛 + 𝑓𝑝 + 𝑓𝑛
  22. 22. Azure Machine Learning Studio: Diabetes Predictor and Sentiment Analysis MakeMagicHappen();
  23. 23. Deploy Experiment as WebService
  24. 24. Deploy Experiment as WebService
  25. 25. AI Powered Spreadsheets
  26. 26. Azure Machine Learning Notebooks
  27. 27. Azure Machine Learning Notebooks MakeMagicHappen();
  28. 28. Azure Machine Learning DataScience LifeCycle
  29. 29. How the Azure Machine Learning service works: architecture and concepts
  30. 30. Microsoft's new "WSYP" Program
  31. 31. Azure Machine Learning Service
  32. 32. Local machine Scale up to DSVM Scale out with Spark on HDInsight Azure Batch AI (Coming Soon) ML Server Experiment Everywhere A ZURE ML EXPERIMENTATION Command line tools IDEs Notebooks in Workbench VS Code Tools for AI
  33. 33. Manage project dependencies Manage training jobs locally, scaled-up or scaled-out Git based checkpointing and version control Service side capture of run metrics, output logs and models Use your favorite IDE, and any framework Experimentation service U S E T H E M O S T P O P U L A R I N N O V A T I O N S U S E A N Y T O O L U S E A N Y F R A M E W O R K O R L I B R A R Y
  34. 34. DOCKER Single node deployment (cloud/on-prem) Azure Container Service Azure IoT Edge Microsoft ML Server Spark clusters SQL Server Deploy Everywhere A ZURE ML MODEL MANAGEMENT
  35. 35. Deployment and management of models as HTTP services Container-based hosting of real time and batch processing Management and monitoring through Azure Application Insights First class support for SparkML, Python, Cognitive Toolkit, TF, R, extensible to support others (Caffe, MXnet) Service authoring in Python Manage models
  36. 36. Azure Machine Learning Visual Studio Tools for AI
  37. 37. VS Code extension with deep integration to Azure ML End to end development environment, from new project through training Support for remote training Job management On top of all of the goodness of VS Code (Python, Jupyter, Git, etc) VS Code Tools for AI
  38. 38. Machine Learning & AI Portfolio When to use what? What engine(s) do you want to use? Deployment target Which experience do you want? Build your own or consume pre-trained models? Microsoft ML & AI products Build your own Azure Machine Learning Code first (On-prem) ML Server On- prem Hadoop SQL Server (cloud) AML services (Preview) SQL Server Spark Hadoop Azure Batch DSVM Azure Container Service Visual tooling (cloud) AML Studio Consume Cognitive services, bots
  39. 39. What’s new with Azure Machine Learning
  40. 40. Microsoft AI platform Azure AI Services Azure Infrastructure Tools
  41. 41. Bruno Capuano Innovation Lead @Avanade @elbruno |