Using Azure Machine Learning to Detect Patterns in Data from Devices
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This session is about learning how to use Microsoft Azure Machine Learning with the devices in order to detect data patterns. This session will cover an introduction to Machine Learning, and different algorithms used to detect data patterns. The algorithms discussed will be nearest neighbor, probabilistic learning, decision trees, and neural networks. It will also cover data that comes from devices like the Kinect for Windows device. The session will show basic demos and data coming from the device. The session will then drill down into how to incorporate Azure Machine Learning features into an application to detect data patterns in real time.
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Using Azure Machine Learning to Detect Patterns in Data from Devices
- 1. Sponsored & Brought to you by Using Azure Machine Learning to Detect Patterns in Data from Devices Dwight Goins https://www.linkedin.com/pub/dwight-goins/1/836/591
- 2. Confidential ©Nimbo Technologies Inc. Microsoft Azure Machine Learning USING MICROSOFT AZURE MACHINE LEARNING WITH DEVICES
- 3. AGENDA • 5 Reasons to use Azure ML with Devices • IoT and Cloud based Devices • Need for processing data • Machine Learning (ML) Overview • ML Approaches • Implementing ML Algorithms • Microsoft Azure Machine Learning • Using a Device with Azure ML
- 4. 5 REASONS • Recognize Data Patterns • Predict Actions and Events • Find similar data - group • Rank data • Quickly Experiment with Innovative Ideas
- 5. IOT AND CLOUD DEVICES
- 6. NEED FOR PROCESSING DATA
- 7. MACHINE LEARNING OVERVIEW • Apply Math and Science algorithms to previous data to predict, reason, discover new patterns and data • Many Approaches
- 8. LAZY LEARNER APPROACH • Nearest neighbor • kNN
- 9. PROBABILISTIC LEARNING APPROACH • Describing uncertainty • 70% chance of rain • Spam filtering • Naive Bayes
- 10. DECISION TREES AND RULES • Series of logical decisions • Credit scoring, medical diagnosis • Recursive partitioning • C5.0
- 11. REGRESSION MODELS • Forecasting Numeric Data • Process of fitting “Lines” to data – “Regression to the mean” • Ordinary Least Squares
- 12. NEURAL NETWORK • Idea Based on How Brain Neurons work • Input, Output, Sum, Logic
- 13. IMPLEMENTING ML IN CODE • Data Scientist • PhD in Math • Steps • R – Models & Tests • Implement in C++/Python/Ruby/.Net with ENCOG
- 14. IMPLEMENTING ML IN CODE • R – Demo
- 15. MAML – MICROSOFT AZURE ML - DEMO
- 16. INTRODUCING MAML • Not that one this one…
- 17. USING THE KINECT DEVICE WITH MAML • Kinect Device has 4 sensors • Color • IR • Depth • Microphone • Generates A LOT of data • Demo
- 18. STEPS TO DETECT DATA PATTERNS • Record Tests into Spreadsheets • Use Azure ML • Expose Azure ML Web Services • Build Application
- 19. APPLICATION PROCESS FLOW Device • IOT Devices – Events & Data • Kinect – Events & Data Connectivity • Event Hubs • Service Bus REST/WCF Storage • SQL DB • BLOB • Doc DB Analytics • Azure ML • Steram Analytics • HDInsight • Data Warehouse Presentation/Action • App Service • PowerBI • Notification hubs • Mobile Services • BizTalk services
- 20. RECAP 5 REASONS • Recognize Data Patterns • Predict Actions and Events • Find similar data - group • Rank data • Quickly Experiment with Innovative Ideas
- 21. CALL TO ACTION • Studio.azureml.net – Register and start playing • Windows 10 – IoT devices • KinectForWindows.com • Dgoins.wordpress.com • Nimbo.com
- 22. REFERENCES • R – http://r-project.org • ENCOG – HeatonResearch.com/encog • Azure ML – http://Studio.azureml.net • MVP Virtual Conference - aka.ms/mvpvconf • Dr. James McCaffrey – MSDN Articles https://msdn.microsoft.com/en- us/magazine/hh975375.aspx • Dwight Goins Blog – http://dgoins.wordpress.com
Editor's Notes
- Using Azure Machine Learning to Detect Patterns in Data from Devices
- ML recognizes data patterns – Devices have a lot of data you need some engine to identify and classifly. ML can help predict actions or guess estimations (regression) based on data/events – Devices gather action data and events ML can find hidden features and help classify data – Sensors has a lot of data IoT is a buzzword for devices with many sensors – ML can make sense of the data, group and rank data. Use ML as a testbed for POC’s and experimental projects and solutions and help reduce or logically deduct conclusions (Classification, Regression, Ranking, Clustering, Dimensionality)
- ML recognizes data patterns – Devices have a lot of data you need some engine to identify and classifly. ML can help predict actions or guess estimations (regression) based on data/events – Devices gather action data and events ML can find hidden features and help classify data – Sensors has a lot of data IoT is a buzzword for devices with many sensors – ML can make sense of the data, group and rank data. Use ML as a testbed for POC’s and experimental projects and solutions and help reduce or logically deduct conclusions (Classification, Regression, Ranking, Clustering, Dimensionality)
- ML recognizes data patterns – Devices have a lot of data you need some engine to identify and classifly. ML can help predict actions or guess estimations (regression) based on data/events – Devices gather action data and events ML can find hidden features and help classify data – Sensors has a lot of data IoT is a buzzword for devices with many sensors – ML can make sense of the data, group and rank data. Use ML as a testbed for POC’s and experimental projects and solutions and help reduce or logically deduct conclusions (Classification, Regression, Ranking, Clustering, Dimensionality)
- ML recognizes data patterns – Devices have a lot of data you need some engine to identify and classifly. ML can help predict actions or guess estimations (regression) based on data/events – Devices gather action data and events ML can find hidden features and help classify data – Sensors has a lot of data IoT is a buzzword for devices with many sensors – ML can make sense of the data, group and rank data. Use ML as a testbed for POC’s and experimental projects and solutions and help reduce or logically deduct conclusions (Classification, Regression, Ranking, Clustering, Dimensionality)