v like velocity, predicting in real-time with azure ml

V like Velocity, Predicting in Real-Time with Azure MLBarbara Fusinska@BasiaFusinska

About me

ProgrammerMachine Learning

Data Solutions Architect@BasiaFusinska

Agenda• Aircraft Predictive maintenance

• Use Case description• Stream processing challenges• Machine Learning Model

• Solution Architecture• Azure Architecture• Stream processing• Applying intelligence to streams

Aerospace Predictive Maintenance Engines Sensors Analysis

Will the device fail in 2 weeks, 4 weeks or will

not fail during this time?

Use CaseWhen will the device

break?

Will the device fail in the next 2 weeks?

VelocityHow is it different?

Big Data 4Vs

• Untrusted• Uncleansed

• Speed of generation• Rate of analysis

• Unstructured• Semi-structured• Structured

• Click stream• Active/passive sensors• Logs• Events• Speech• Social media• Traditional

Volume Variety

VeracityVelocity

Big Data & AzureIntelligence

Dashboards & Visualizations

Information Management

Big Data Stores Machine Learning and Analytics

CortanaEvent HubHDInsight (Hadoop and Spark)

Stream Analytics

Data Sources

Apps

Sensors and devices

Data Intelligence Action

People

Automated Systems

Apps

Web

Mobile

Bots

Bot Framework

SQL Data WarehouseData Catalog

Data Lake Analytics

Data Factory Machine LearningData Lake Store Blob Store

Cognitive Services

Power BI

Batch vs Real-time

Stream processing challenges• Data Ingestion• Stream Processing• Applying intelligence to the

stream• Aggregations• Data sink

Machine Learning

Machine LearningModel

Model Training

PublishedMachine Learning

Model

Prediction

Training data

Publish model

Test stream

Scores

Machine Learning workflowData

preparation Data split

Machine Learning algorithm

Trained model Score

Clean data

Training data Test data

Azure Machine Learning ServiceData -> Predictive model -> Operational web API in minutes

Blobs and TablesHadoop (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

Azure Machine Learning StudioIncome predictionDemo

Will the device break in the next 2 weeks?

cycle 11 cycle 12 cycle 13 cycle 14 cycle 150

1

2

3

4

5

6

Sensor reads

s1 s2 s3

Device37

Cycle s1 ... s21 failed

1 518.67 23.419 0

2 518.67 23.4236 0

3 518.67 23.3442 0

...

134 518.67 23.1295 0

135 518.67 23.4085 1

(s, avg, stdev)

https://gallery.cortanaintelligence.com/SolutionTemplate/Predictive-Maintenance-for-Aerospace-1https://gallery.cortanaintelligence.com/Collection/Predictive-Maintenance-Template-3

Aircraft Predictive Maintenan

ceUse Case

Solution Architecture

Stream Analytics

Event Hub DocumentDB

Machine Learning Web AppML Model TrainingTraining data

Retrain

Sensors data

simulationData generator

Event Hub configurationData Ingestion

DocumentDB configurationStoring Maintenance Predictions

Stream analytics configurationMaintenance prediction

Stream Analytics – Source and SinkSELECT

DeviceId, result.ScoredLabels,cycle, seeting1, …, s1, …, s21,a1, …, a21, sd1, …, sd21

INTO maintenanceFROM predict

Aggregation in Stream Analytics

SELECT avg(s1) as a1FROM CallStreamGROUP BY id, TumblingWindow(minute, 1)

TIMESTAMP BY T

Stream Analytics – AggregationsWITH aggregate AS (SELECT

id,avg(s1) AS a1, …, avg(s21) AS a21, stdev(s1) AS sd1, …, stdev(s21) AS sd21

FROM CallStream AS sGROUP BY id, TumblingWindow(minute, 1))

Join in Stream AnalyticsSELECT c.s1, a.a1FROM CallStream cJOIN aggregate aON c.id = a.id

and DATEDIFF(minute, c, a)BETWEEN 0 and 1

Stream Analytics – Join aggregationsWITH predict AS (SELECT

s.id, s.cycle, s.seeting1, …, s.s1, …, s.s21,a.a1, …, a.a21, a.sd1, …, a.sd21,predmain(s.cycle, s.seeting1, …, s.s1, …, s.s21,a.a1, …, a.a21, a.sd1, …, a.sd21) AS result

FROM CallStream AS sjoin aggregate AS aON s.id = a.idAND DATEDIFF(minute, s, a) BETWEEN 0 AND 1

)

Monitoring DashboardReading Predictions from DocumentDB

Retraining scenarioDemo

Scaling web services• Azure Classic Portal

• 20-200 concurrent requests• Azure ML optimization• Multiple endpoints for the

same web service

• New web services• Production web services• Pricing Plans• 1,000 – 50,000,000 request

per month

Keep in touch

BarbaraFusinska.com@BasiaFusinska