Azure Machine Learning studio is a drag-and-drop tool for building, testing, and deploying predictive analytics solutions. It publishes models as web services that can be consumed by apps or tools like Excel. Machine Learning Studio is where data science, predictive analytics, cloud resources, and data meet. It provides an interactive workspace to develop models by manipulating and analyzing data with various functions. Modules in Machine Learning Studio perform algorithms on data, ranging from data ingestion to training, scoring, and validation. Data Science Virtual Machines on Azure provide pre-configured environments for machine learning with tools like Python, R, and Spark.

1. Eng Teong Cheah
MVP Visual Studio &
Development Technologies
Introduction to
Azure Machine
Learning

2. Agenda
What is machine learning studio
Developing and hosting application

3. What is machine
learning studio

4. What is machine learning studio
Azure Machine Learning studio is a collaborative,
drag-and-drop tool you can use to build, test, and
deploy predictive analytics solutions on your data.
Machine Learning Studio publishes models as web
services that can easily be consumed by custom apps
or BI tools such as Excel.

5. What is machine learning studio
Machine Learning Studio is where data science,
predictive analytics, cloud resources, and your data
meet.

6. Machine Learning Studio Interactive
Workspace
To develop a predictive analysis model, you typically
use data from one or more sources, transform and
analyze that data through various data manipulation
and statistical functions, and generate a set of results.
Developing a model like this is an interactive process.

7. Machine Learning Studio Interactive
Workspace
As you modify the various functions and their
parameters, your results converge until you are
satisfied that you have trained effective model.

8. Modules
A module is an algorithm that you can perform on
your data.
Machine Learning Studio has a number of modules
ranging from data ingress functions to training,
scoring, and data validation processes.

9. Modules
Here are some examples of included modules:
-Convert to Attribute-Relation File Format (ARFF)
-Compute Elementary Statistics
-Linear Regression
-Score Model

10. Developing & hosting
application

11. Developing and hosting application
Using Data Science Virtual Machine
-Pre-configure environments for data scientists
-Seamless integration with Azure services
-Any VM size, horizontal scale out with Azure Batch,
burst compute
-User extensible

12. Pre-Configured environments in the cloud
for Data Science and AI Development
DSVMs are Azure Virtual Machine images, pre-
installed, configured and tested with several popular
tools that are commonly used for data analytics,
machine learning and AI training.

14. What included
Anaconda Python R Server Jupyter Notebooks Visual Studio Excel
SQL Server PowerBI Python Microsoft Cognitive
Toolkit
Apache Spark

15. Demo
Clone and run simple experiment

16. Resources
TutorialsPoint
Microsoft Docs
Lecture Collection | Convolutional Neural Networks for
Visual Recognition(Spring 2017)
Python Numpy Tutorial
Image Credits: @ashleymcnamara

17. Thank you
Eng Teong Cheah
Microsoft MVP Visual Studio & Development Technologies
Twitter: @walkercet
Github: https://github.com/ceteongvanness
Blog: https://ceteongvanness.wordpress.com/
Youtube: http://bit.ly/etyoutubechannel