Cloud Expo 2014 Making the Internet of Things (IoT) Charming
IoT Evolution Expo- Machine Learning and the cloud
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Transcript of IoT Evolution Expo- Machine Learning and the cloud
By: Adj. Prof. Giuseppe Mascarella – Brief Bio
• [email protected]• Linkedin: www.linkedin.com/in/giuseppemascarella
Machine Learning and the Cloud
1. What Is Machine Learning?
2. Where do we deploy machine learning and what cloud services are out there to support it?
3. What are the trends in deploying these systems and what are the benefits for IT?
4. Do you have a IoT Machine Learning Case Study in the Cloud?
What Are The Questions We Want To Address?
What has changed
Source: www.microsoft.com
Data is out there and is free (Open data). It provides no competitive advantages. Finding patterns in data is the holy grail (the oil in a barrel!)
Is ML Only Data Patterns and Forecasts?
It’s interface is based on ‘machine learning’ i.e. it learns and becomes better with use. This will be common with ALL products and will determine the competitive advantage of companies. Its a winner takes all game! Every product will have a ‘self learning’ interface/component and the product which learns best will win!
What Is Machine Learning?
The Internet of Things (IoT) is a network with the aim to connect physical objects that contain embedded technology to communicate, sense or interact with their internal states or the external environment.
Machine learning is the ability of a agent to vary the outcome of a situation or behavior based on knowledge or observation which is essential for IoT solutions.
3. What are the trends in deploying these systems?
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
Do you have a IoT Machine Learning Case Study in the Cloud?
IoT Scenario
Predictive Maintenance in IoT Traditional Maintenance
Goal Improve production and/or maintenance efficiency at lowest cost
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
Sample Existing Predictive Maintenance Journey
Develop ML model (MATLAB) alongside local university
Optimise code Reduce runtime
Build evaluation module
Refine model parameters
Years
Develop user web front end
IoT Predictive Maintenance – Qantas Airways
~24,000 sensors
Qantas A380 Fleet
Technical Delays1
2
$65M+per A380
50%Technical Delays400-
700Fault/warning messages/day
have potential for predictive modelling
Microsoft Cloud Azure ML Journey
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
Stay ahead of the curve with Cortana Intelligence Suite
Business apps
Custom apps
Sensors and devices
People
Automated systems
Data Machine LearningEcosystem
Cortana Intelligence
Action
Apps
The IoT Ecosystem Around MLIntelligence
Dashboards & Visualizations
Information Management
Big Data Stores Machine Learning and Analytics
CortanaEvent HubsHDInsight (Hadoop and Spark)
Stream Analytics
Data Action
People
Automated
Systems
Apps
Web
Mobile
Bots
Bot Framework
SQL Data WarehouseData Catalog Data Lake
Analytics
Data Factory Machine LearningData Lake
StoreCognitive Services
Power BI
Data Sources
Apps
Sensors and devices
Data
Machine LearningEcosystem
In The Cloud
Machine Learning & Data Science Process
Source: www.microsoft.com
Machine Learning Terminology1. Training Data: A set of samples2. Features: The column in our data set for
ML3. Label/Target: Historical outcome for set
of data4. Feature Engineering/Munging:
Manipulating data to come to a training data set
5. Learner: ML Algorithm
Data Science Process
DefineScope
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
Load The Data
Labeling Features Engineering
Build The Model
Load The Data: Data Sources
The failure history of a machine or a component
The repair historyPrevious 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
Data Science Process
DefineScope
Engineer Feature
Rolling Aggregates
Tumbling Aggregates
Static Features
E.g. Mean, Min, Max for every hour in the last 3 hours
E.g. Mean, Min, Max over the last 3 hours
E.g. Years in service, model
1. Selected raw features 2. Aggregate features
Data Science Process
DefineScope
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
Build The Modelo Regression: Predict the Remaining Useful Life
(RUL)o Binary classification: Predict if an asset will
fail within certain time frame (e.g. 7 days). o Multi-class classification: Predict if an asset
will fail in different time windows:
1. fails in window [1, w0] days; 2. fails in the window [w0+1,w1] days; 3. not fail within w1 days
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
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
1. What Is Machine Learning?
2. Where do we deploy machine learning and what cloud services are out there to support it?
3. What are the trends in deploying these systems and what are the benefits for IT?
4. Do you have a IoT Machine Learning Case Study in the Cloud?
The Questions Addressed in This Session
Adj. Prof. Giuseppe Mascarella – Brief Bio
• Contact us for 1 free consultation: [email protected]
• Twitter: @giuseppeHighTec• Linkedin: www.linkedin.com/in/giuseppemascarella
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Appendix