Overview of Machine Learning and Deep Learning. Brief introduction to different types of BI Reporting tools like Power BI, SSMS, Cortana, Azure ML, TenserFlow and other tools.

2. NISHAN ARYAL
Cloud App Developer
CTO – Innovation Lab
“Let’s learn together
and share together”
is what I believe
www.aryalnishan.com.np

3. If you invent a
breakthrough in
artificial intelligence,
so machines can learn,
that is worth 10
Microsoft

4. What is Machine Learning?

8. What is Machine Learning?
• Using Computer Power to gain insight into
data that might other be elusive
• Credit Card Fraud Detection
• Online Shopping recommendations
• Self driving Cars and Tesla Motors
• Tuning data into Solutions
• Machine Learning Strategies
• Supervised Learning
• Unsupervised Learning

9. Traditional Programming
Program Data Output

10. Machine Learning
Data Output Program

11. Prepare
Data
• Import data
• Pre-process data
Train
Model
• Select Learning Algorithm and Build Model
• Experiment/Iterate/Evaluate
Make
Operational
• Prepare Model for Deployment
• Deploy and call fro Applications

12. You have been using ML since long

13. Machine Learning/Deep Learning or AI?

14. Bing maps
ships with ML
Traffic
Prediction
Service
Microsoft Kinect
can watch users
gestures
Computers
work on
users behalf,
filtering junk
email
Successful real-time
speech-to-speech
translation
Microsoft Search
Engine build with
Machine Learning
Enables data
mining of
databases
MICROSOFT MACHINE LEARNING HISTORY

15. SQL Server
Data Mining
Spam filtration Gestures
understanding
in Microsoft
Kinect
Azure Machine
Learning
Using Data
Mining in
search engines
Bing Maps started
to use ML for
traffic estimate
Voice recognition
Microsoft & Machine Learning
1999 201220082004 201420102005

16. Years of Innovation

19. Microsoft Azure
Machine Learning
 Reduced Complexity
 Access Through Web Browser, no
need to install any thing
 Collaborate work with anyone
 Visual composition, easy to use,
No Coding
 Good storage of Algorithm (Use
in Bing search, Xbox..)
 Have good support for R studio,
Python and Jupyter notebook

20. Azure Machine Learning Solution

21. Azure ML Services
pre-configured environment for deep learning using GPU instances
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.

22. 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

23. Azure ML Studio
 Browser-based environment supporting general users
and data scientists
 Immutable library of models including search, discover,
and reuse
 Wide range of features, machine learning algorithms,
and modeling strategies
 Ability to quickly deploy models as Azure web services
to the ML API service
New experiment flow
Streamlined experiment page
New visualization for data tables
Azure ML Studio

24. Azure ML API
 Web-service and REST-based for easy creation and fast
deployment
 Allows general users and data scientists to run models
as web services in minutes
 Build apps that are easily accessible as web services,
app plug-ins, or even mobile apps
 Supports advanced data science, including R coding
and 350 R packages included
Custom data ingress and egress
Extends ML Studio with customization
Rich functionality – rules engine, R
support, optimizer, simulation

25. AZURE MACHINE LEARNING STUDIO
• https://studio.azureml.net
• Online IDE to build, test, and deploy machine learning models
• Drag and drop “modules”

28. Azure ML Data Preprocessing

29. GOAL OF MACHINE LEARNING
“The goal of machine learning is to
program computers to use example data
or past experience to solve a given
problem

30. CORTANA ANALYTICS SUITE
successful experience with Azure ML
https://www.youtube.com/watch?v=YxmAEMmwXYU

33. SQL SERVER ANALYSIS SERVICE

34. SSAS Vs Azure ML
Features Usability Cost Support
• End to end Product
• Canned algorithm
• Not possible to change
algorithm
• DMX Code
•More Visual
• Excel Version
•It’s not easy to start
•All users can use
If you
purchase
SQL Sever:
Free
Few books and
small online
community
• Current and up to date
algorithm
• Integration with R and
Python
• Cloud base
• REST format
• Hard to interpret
• Drag and Drop UI
• Customize the
Algorithm
Free version,
limited
options
More online
Community

35. OTHER RESOURCES