Slides prepared for K-MUG Azure Machine Learning event on 9 July 2015 at Kochi

1. praveen nair
blog.ninethsense.com
@ninethsense

2. praveen nair
blog.ninethsense.com
@ninethsense
What is in this presentation?
1. Why Machine Learning?
2. Cortana Analytics Suite
3. High Level view of Azure ML
4. Azure Machine Learning Solution
5. Azure ML Learning Studio Demo
6. Azure ML Market trends
Source: All the slides copied form Sales Microsoft Presentations

3. What is Machine Learning?
“Computing systems that improve with experience”
Predictive Analytics → “a way to scientifically use
the past to predict the future”

4.  Are you concerned with timely maintenance of in-service equipment?
 Are scheduled or routine maintenance operations becoming too costly?
Predictive
maintenance
 Is your current data analysis infrastructure deep enough to support fraud
detection?
 If not, what are the primary reasons: cost, scale, data science?
Frauddetection
 Do you currently have social mechanisms in place to promote product
recommendations across your site and product portfolio?
 If so, are these measures performing well and are you able to analyze
customer recommendations for optimal insight?
Product
recommendation
 Do you feel a deeper understanding of historical customer data will help your
business predict demand, assess future capacity, or make pricing decisions?
 Are you currently using historical data to accurately predict future demand?
Demandforecasting
 Do you need more accurate data on customer churn and turnover?
 Are you losing revenue through customer cancellations?
Churnanalysis
Sample questionsNeeds
Supplychain
optimization
 Can your organization generate accurate supply chain success metrics, such as
Gross Margin Return on Inventory Invested (GMROII) or operational expense
minimization?

5. Azure Machine Learning
Microsoft Azure ML → simplifies data analysis and
empowers you to find the answers to your business
needs
A fully managed service that you can use to create,
test, operate, and manage predictive analytic
solutions in the cloud

6. Cortana Analytics Suite

7. Microsoft Azure Machine Learning
Data to model to web services in minutes
Devices Applications Dashboards
Data
Storage space
Business challenge Business valueModeling Deployment
 HDInsight
 SQL Server VM
 SQL DB
 Blobs and tables
Cloud
Local
Clients
Integrated development
environment for Machine
Learning
ML
Studio
http://studio.azureml.net
Web
API
Model is now a web service
Microsoft Azure
Marketplace
Monetize this API
Delivering advanced analytics
 Desktop files
 Excel spreadsheet
 Other data
files on PC

8. Deploy in minutes
Operationalize models
as web services with a
single click; monetize in
Azure Machine Learning
Marketplace
Flexible
Built-in collection of
best of breed
algorithms with no
coding required. Drop
in custom R or use
popular CRAN packages
Integrated
Drag, drop, and connect
interface. Data sources
with just a drop down
run across any data.
Fully managed
No software to install,
no hardware to manage;
all you need is an Azure
subscription.
Built for a cloud-first, mobile-first world
Microsoft Azure Machine Learning

10. The Azure Machine Learning solution
Azure ML Studio
 Browser-based
 Designed for people without deep data science
backgrounds
 Supports deep science scenarios – R support,
multiple models
Azure Marketplace
 Drag-and-deploy
 Fast monetization of ML solutions and APIs
 Quick source for free and third-party Azure ML
APIs
Azure cloud services
 No software to install or infrastructure needed
 Nearly unlimited file repositories via Azure Storage
 Supports Azure data-related services – HDInsight,
SQL Database
Azure ML API
 REST-based web service
 Supports best-in-class algorithms
 Reduces time from model experimentation to
production
https://studio.azureml.net/

11. Write out the results:
Azure blob
SQL Database
Azure table
Hive Query (Hadoop)
Load Data from…

12. Demo Time
Azure ML Learning Studio

13. Cortana Intelligence Gallery
• https://gallery.cortanaintelligence.com/
• Community of Developers and Data Scientists

14. T4T3T2T1
CLOUD
PLATFORM
VENDORS
OPEN
SOURCE
MEGA
VENDORS
STARTUPS
IBM SPSS
Python
Amazon (Spark ML)
Alteryx
Google Prediction API
R
0xdata
SAS
SAP (InfiniteInsight)
Dell (Statsoft)
Oracle Data Miner
Predixion
bigML
RapidMiner
GraphLab
Skytree
Guavus
Spark ML
Weka
Knime
FICO Model Builder
Salford
Matlab Tibco Spotfire

15. Gartner 2016 Magic Quadrant for
Advanced Analytics Platforms

16. Limitations
• Still improving…
• Your data is in MSFT Data Centers / Data Privacy
• Cloud & Data Safety
• Only R & Python supported (currently)

17. Thank You