IoT And Machine Learning

PPT From the IoT Evolution Expo ibn Fort Lauderdale, FL. USA
-What Is Machine Learning?
-Where do we deploy machine learning and what software and cloud services are out there to support it? 
-What are the trends in deploying these systems and what are the benefits for IT?
-Do you have a IoT Machine Learning Case Study in the Cloud?

Transcript

1. • Contact us for 1 free consultation: giuseppe@valueamplify.com • Linkedin: www.linkedin.com/in/giuseppemascarella
3. Source: www.microsoft.com
4. Finding patterns in data is the holy grail (the oil in a barrel!)
6. What Is Machine Learning?
7. 2. Directed Knowledge where knowledge created elsewhere (by a central authority) will be used to modify edge behavior Cloud 1. Observed Knowledge which will modify behavior based on local learning (context) Edge 3. Sensor Fusion Knowledge the combining of sensory data and data delivery orchestration such that the resulting information is in some sense better than would be possible when these sources were used individually. See Kalman filter
9. IoT Scenario Predictive Maintenance in IoT Traditional Maintenance Goal Improve production and/or maintenance efficiency Ensure scheduled maintenance has been done Data -Data stream (time varying features) -Multiple data sources Tasks completed to be done Tasks -Failure prediction -Fault/failure detection & diagnosis, - Recommendation maintenance actions -Fault/failure tracking -Procedure for Diagnosis
10. Develop ML model (MATLAB) alongside local university Optimise code Reduce runtime Build evaluation module Refine model parameters Develop user web front end IoT Predictive Maintenance – Qantas Airways ~24,000 sensors Qantas A380 Fleet Technical Delays 12 $65M+ per A380 50% Technical Delays 400-700 Fault/warning messages/day have potential for predictive modelling Configure model in AML PM template Evaluate & refine model data & parameters Visualize results in Power BI Months /year Orchestrate data pipeline in Azure Data Factory Source: www.microsoft.com
12. Stay ahead of the curve with Cortana Intelligence Suite Business apps Custom apps Sensors and devices People Automated systems Data Machine Learning Ecosystem Cortana Intelligence Action Apps
13. The IoT Ecosystem Around ML Intelligence Dashboards & Visualizations Information Management Big Data Stores Machine Learning and Analytics Cortana Event Hubs HDInsight (Hadoop and Spark) Stream Analytics Data Action People Automated Systems Apps Web Mobile Bots Bot Framework SQL Data Warehouse Data Catalog Data Lake Analytics Data Factory Machine Learning Data Lake Store Cognitive Services Power BI Data Sources Apps Sensors and devices Data Machine Learning Ecosystem
14. In The Cloud
16. Source: www.microsoft.com
18. Define Scope
19. Good Scope for ML Experiment Question is sharp. Data measures what they care about. Data is connected. Data is accurate. A lot of data. The better the raw materials, the better the product. E.g. Predict whether component X will fail in the next Y days; clear path of action with answer E.g. Identifiers at the level they are predicting E.g. Will be difficult to predict failure accurately with few examples E.g. Failures are really failures, human labels on root causes; domain knowledge translated into process E.g. Machine information linkable to usage information
20. Load The Data Labeling Features Engineering Build The Model
21. Load The Data: Data Sources The failure history of a machine or a component The repair history Previous maintenance records, Components replaced Maintenance opeators Performance data collected from sensors. FAILURE HISTORY REPAIR HISTORY MACHINE CONDITIONS The features of machine or components, e.g. production date, technical specifications. Environmental features that may influence a machine’s performance, e.g. location, temperature, other interactions. The attributes of the operator who uses the machine, e.g. driver. MACHINE FEATURES OPERATING CONDITIONS OPERATOR ATTRIBUTES
22. Feature Engineering 1. Selected raw features 2. Aggregate features
27. Modelling Techniques Predict failures within a future period of time BINARY CLASSIFICATION Predict failures with their causes within a future time period. Predict remaining useful life within ranges of future periods MULTICLASS CLASSIFICATION Predict remaining useful life, the amount of time before the next failure REGRESSION Identify change in normal trends to find anomalies ANOMALY DETECTION
28. Algorithms
30. https://channel9.msdn.com/blogs/Cloud-and-Enterprise- Premium/Data-Science-of-Predictive-Maintenance
32. Confusion Matrix
33. “Most IoT data are not used currently… the data that are used today are mostly for anomaly detection and control, not optimization and prediction, which provide the greatest value.”
35. Acknowledgements • We utilized the following publically available data to help us generate realistic data for the demo shown. We received assistance in creating this solution as a result of this repository and the donators of the data: “A. Saxena and K. Goebel (2008). "PHM08 Challenge Data Set", NASA Ames Prognostics Data Repository (http://ti.arc.nasa.gov/project/prognostic-data-repository), NASA Ames Research Center, Moffett Field, CA.” • McKinskey Global Institute, The Internet of Things: Mapping the Value beyond the hype • Microsoft Cortana Gallery Experiments
36. Learn and try yourself! • Learn from Cortana Analytics Gallery • Solution package material – deploy by hand to learn here • Try Cortana Analytics Solution Template – Predictive Maintenance for Aerospace in private preview • Try Azure IOT pre-configured solution for Predictive Maintenance • Read the Predictive Maintenance Playbook for more details on how to approach these problems • Run the Modelling Guide R Notebook for a DS walk- through
37. • Contact us for 1 free consultation: giuseppe@valueamplify.com • Twitter: @giuseppeHighTec • Linkedin: www.linkedin.com/in/giuseppemascarella