By: Adj. Prof. Giuseppe Mascarella – Brief Bio

• giuseppe@valueamplify.com• 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

EvaluatingConfusion Matrix

Dogs

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: giuseppe@valueamplify.com

• Twitter: @giuseppeHighTec• Linkedin: www.linkedin.com/in/giuseppemascarella

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