Building predictive models in Azure Machine Learning
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The document outlines a comprehensive overview of machine learning capabilities on Azure, highlighting its scalable architecture and easy deployment via a user-friendly interface. It discusses the use of known data to develop predictive models and emphasizes various types of machine learning tasks, such as classification and regression. Additionally, it mentions monetization opportunities through a machine learning marketplace and quick integration with APIs and data sources.
Building predictive models in Azure Machine Learning
- 10. Value
- 12. R Python APIs
- 13. Fully managed Integrated Flexible Deploy in minutes No software to install, no hardware to manage, all you need is an Azure subscription. Drag, drop and connect interface. Data sources with just a drop down; run across any data. Built-in collection of best of breed algorithms with no coding required. Drop in custom R or use popular CRAN packages. Operationalize models as web services with a single click. Monetize in Machine Learning Marketplace.
- 14. Get/Prepare Data Build/Edit Experiment Create/Updat e Model Evaluate Model Results Publish Web Service Build ML Model Deploy as Web ServiceProvision Workspace Get Azure Subscription Create Workspace Publish an App Azure Data Marketplace
- 15. Blobs and Tables Hadoop (HDInsight) Relational DB (Azure SQL DB) Data Clients Model is now a web service that is callable Monetize the API through our marketplace API Integrated development environment for Machine Learning ML STUDIO
- 18. 50°F 30°F 68°F 95°F1990 48°F 29°F 70°F 98°F2000 49°F 27°F 67°F 96°F2010 ? ? ? ?2020 … … … …… Known data Model Unknown data Weather forecast sample Using known data, develop a model to predict unknown data.
- 19. 90°F -26°F 50°F 30°F 68°F 95°F1990 48°F 29°F 70°F 98°F2000 49°F 27°F 67°F 96°F2010 Using known data, develop a model to predict unknown data. Predict 2020 Summer
- 21. Classify a news article as (politics, sports, technology, health, …) Politics Sports Tech Health Using known data, develop a model to predict unknown data.
- 22. Using known data, develop a model to predict unknown data. Documents Labels Tech Health Politics Politics Sports Documents consist of unstructured text. Machine learning typically assumes a more structured format of examples Process the raw data
- 23. Using known data, develop a model to predict unknown data. LabelsDocuments Feature Documents Labels Tech Health Politics Politics Sports Process each data instance to represent it as a feature vector
- 24. Known data Data instance i.e. {40, (180, 82), (11,7), 70, …..} : Healthy Age Height/Weight Blood Pressure Hearth Rate LabelFeatures Feature Vector
- 25. Using known data, develop a model to predict unknown data. Documents Labels Tech Health Politics Politics Sports Training data Train the Mode l Feature Vectors Base Model Adjust Parameters
- 26. Known data with true labels Tech Health Politics Politics Sports Tech Health Politics Politics Sports Tech Health Politics Politics Sports Model’s Performance Difference between “True Labels” and “Predicted Labels” True labels Tech Health Politics Politics Sports Predicte d labels Train the Model Split Detac h +/- +/- +/-
- 31. • Supervised learning examples • This customer will like coffee • This network traffic indicates a denial of service attack • Unsupervised learning examples • These customers are similar • This network traffic is unusual
- 32. Classification Regression Anomaly Detection Clustering Supervised Supervised SupervisedUnSupervised
- 34. Classification
- 35. Clustering
- 36. Regression
- 37. • Regression problems • Estimate household power consumption • Estimate customer’s income • Classification problems • Power station will|will not meet demand • Customer will respond to advertising
- 38. • Binary examples • click prediction • yes|no • over|under • win|loss • Multiclass examples • kind of tree • kind of network attack • type of heart disease
- 40. access almost any type of application Azure API Management + AML WS