1.
James Serra
Big Data Evangelist
Microsoft
JamesSerra3@gmail.com

2.
About Me
 Microsoft, Big Data Evangelist
 In IT for 30 years, worked on many BI and DW projects
 Worked as desktop/web/database developer, DBA, BI and DW architect and developer, MDM
architect, PDW/APS developer
 Been perm employee, contractor, consultant, business owner
 Presenter at PASS Business Analytics Conference, PASS Summit, Enterprise Data World conference
 Certifications: MCSE: Data Platform, Business Intelligence; MS: Architecting Microsoft Azure
Solutions, Design and Implement Big Data Analytics Solutions, Design and Implement Cloud Data
Platform Solutions
 Blog at JamesSerra.com
 Former SQL Server MVP
 Author of book “Reporting with Microsoft SQL Server 2012”

3.
Advanced Analytics Defined

4.
Fully
managed
Integrated Best in Class
Algorithms
Deploy in
minutes
No software to install,
no hardware to manage,
and one portal to view
and update.
Simple drag, drop and
connect interface for
Data Science. No need
for programming for
common tasks.
Built-in collection of
best of breed
algorithms. Support for
R and Python for
extensibility.
Operationalize models
with a single click.
Monetize in Machine
Learning Marketplace.

5.
ML
Studio
API
M

6.
Establish mechanisms to conduct data science activities end-to-
end in the cloud or on premises, friction free.
http://aka.ms/adapt

7.
Setup Cloud
Environment
Load Data
Explore Data
Engineer Features
Sample Data
Build Model Deploy Model Consume Model

8.
This is Karl.
Karl owns a company that
operates vending machines in
Washington state.
His job is to make sure that his 100
vending machines are selling drinks
& obtaining revenue.
Karl wants revenue to always
be high & his business to
be profitable

9.
Sadly, vending machine will
occasionally break & may take up
to 7 days to fix, thus hurting sales.
To eliminate this occurrence, Karl must maintain
operations & figure out the best way to utilize
resources in order to optimize revenue.

10.
Azure Cloud Services + Machine Learning to the Rescue!
1. Which Machines Have Failed?
2. Which Machines Will Soon Fail?

11.
• Damage is reported by customer
or during weekly restocking routes
• Technician must be scheduled
to investigate
• Process take up to 8 days to fix
a broken machine
• Sensor data is used to monitor
cooler condition in real-time
• Broken coolers are identified
at time of failure
• Lost sales remain due to
maintenance lead teams
(parts & repair technicians)
• Azure ML predicts where, when,
& what failures will occur based
on sensor data
• Spare parts & repairs can be
scheduled before machines shut
down leading to no lost sales
CURRENT SCENARIO REAL-TIME SENSORS SENSORS&MACHINELEARNING
Days: Days:Days:

12.
Cloud
Event Hubs
ML Studio ML API Service
Microsoft
Azure Portal
Blob Storage

13.
ML Apps
Marketplace
ML Operationalization
ML Studio
ML Algorithms

14.
Social network
analysis
Weather
forecasting
Healthcare
outcomes
Predictive
maintenance
Targeted
advertising
Natural resource
exploration
Fraud
detection
Telemetry data
analysis
Buyer propensity
models
Churn analysis
Life sciences
research
Web app
optimization
Network
intrusion
detection
Smart meter
monitoring

15.
Model Your Way: Open source/our source
Python client library

16.
Deploy in Minutes
Web Service Gallery Discover
Learn
Share
Expand your Reach

17.
• Accessible through a web browser, no software
to install;
• Collaborative work with anyone, anywhere via
Azure workspace;
• Visual composition with end2end support for
data science workflow;
• Best in class ML algorithms; Immutable library of
models, search discover and reuse;
• Extensible, support for R & Python;
• Rapidly try a range of features, ML algorithms
and modeling strategies

18.
Cortana Intelligence Suite
Integrated as part of an end-to-end suite
Action
People
Automated
Systems
Apps
Web
Mobile
Bots
Intelligence
Dashboards &
Visualizations
Cortana
Bot
Framework
Cognitive
Services
Power BI
Information
Management
Event Hubs
Data Catalog
Data Factory
Machine Learning
and Analytics
HDInsight
(Hadoop and
Spark)
Stream Analytics
Intelligence
Data Lake
Analytics
Machine
Learning
Big Data Stores
SQL Data
Warehouse
Data Lake Store
Data
Sources
Apps
Sensors
and
devices
Data

19.
Event Hub
Stores Streaming
Data
Stream Analytics
processes events as they
arrive in the EventHub
AML Model
Web Service
BES endpoint
Power BI / D3
Dashboard
Data for
Real-time
Processing
Aggregations
External Data Azure Services
Azure SQL
Contains Historical Energy
Consumption Data
Real time
data stats
Azure Data Factory
Pipeline invokes AML
Web Service
RealTimeBatch
Example Architecture
Real Time
Telemetry
Data
Azure Data Factory
Pipeline Moves Data
Batch updates
of predictions
AML Model
Web Service
RRS endpoint

20.
Real Time Energy
Consumption Data
(Public Source)
Event Hub
Stores Streaming
Data
Stream Analytics
processes events as they
arrive in the EventHub
AML Model
Web Service
BES endpoint
Power BI / D3
Dashboard
Data for
Real-time
Processing
Data Stream
Job
Hourly Prediction
Updates
External Data Azure Services
Copy to Azure SQL
for batch predictions
Scrape Data
5 mins
Azure WebJob
Runs jobs to scrape
data from public source
Azure SQL
Contains Historical Energy
Consumption Data
Real time
data stats
Azure Data Factory
Pipeline invokes AML
Web Service
RealTimeBatch

21.
Q & A ?
James Serra, Big Data Evangelist
Email me at: JamesSerra3@gmail.com
Follow me at: @JamesSerra
Link to me at: www.linkedin.com/in/JamesSerra
Visit my blog at: JamesSerra.com (where this slide deck is posted via the
“Presentations” link on the top menu)