1. Azure Machine Learning & ML Studio
Machine Learning with simplicity and power of cloud
Vinnie Saini
Data & AI Solution Architect

2. Azure ML & ML Studio Fully managed cloud service for building
Predictive Analytics solutions
Reduces the intricacies of Machine Learning process
Azure ML Studio is a powerful canvas for the
Composition of machine learning experiments
Subsequent operationalization
Consumption as machine learning web services

3. Azure ML Architecture Operational Web API Predictive Model Data
Tables,
Hadoop (HDInsight),
Relational DB(Azure SQL)
Predictive Model
ML Studio
Operational Web API
Clients Interface

4. ready-to-use library of algorithms
studio for building models,
easy way to deploy your model as a web service
Quickly create, test, operationalize, and manage predictive models.

5. Build a Data Science experiment in ML Studio
Machine learning workspace
Dataset format
Upload data
Prepare data
Define features
Create an experiment
Choose and apply Learning Algorithms
Train and Evaluate
Remove one model
Convert the training experiment into a predictive experiment
Deploy the predictive experiment as a web service
Deploy and Access web service

6. Get started with Machine Learning Studio
Initialize a Machine Learning workspace
The experiment has at least one dataset and one module
Import a number of data types into your experiment, depending on what mechanism you use to import data :
Upload a local file as a dataset
Use a module to import data from cloud data services
Import a dataset saved from another experiment
Plain text (.txt)
Comma-separated values (CSV) with a header (.csv) or without (.nh.csv)
Tab-separated values (TSV) with a header (.tsv) or without (.nh.tsv)
Excel file
Azure table
Hive table
SQL database table
R object or workspace file (.RData) etc.
You can execute up to four modules in parallel in an experiment.

7. Metadata in ML Studio
Explicitly specify or change the headings and data types for columns using the Edit Metadata.
Data types recognized by Machine Learning Studio:
String
Integer
Double
Boolean
DateTime
TimeSpan

8. Limits for data upload
Modules in Machine Learning Studio support datasets of up to 10 GB of dense numerical data for common use cases.
If a module takes more than one input, the 10 GB value is the total of all input sizes.
For datasets that are larger than a couple GBs, upload data to Azure Storage or Azure SQL Database, or use Azure HDInsight rather than directly uploading from a local file.
You can also sample larger datasets by using queries from Hive or Azure SQL Database, or you can use Learning by Counts preprocessing before ingestion.

9. ML Studio Demo

10. Deploy and Test the web service
Deploy the web service
Deploy as a Classic web service
Deploy as a New web service
Test the web service
When the web service is accessed, the user's data enters through the Web service input module where it's passed to the Score Model module and scored.
The model expects data in the same format as the original dataset. The results are returned to the user from the web service through the Web service output module.
Test a Classic web service
You can test a Classic web service in Machine Learning Studio or in the Machine Learning Web Services portal.
Test in Machine Learning Studio
On the DASHBOARD page for the web service, click the Test button under Default Endpoint. A dialog pops up and asks you for the input data for the service. These are the same columns that appeared in the original credit risk dataset.
Enter a set of data and then click OK.
Test in the Machine Learning Web Services portal
On the DASHBOARD page for the web service, click the Test preview link under Default Endpoint. The test page in the Azure Machine Learning Web Services portal for the web service endpoint opens and asks you for the input data for the service. These are the same columns that appeared in the original credit risk dataset.
Click Test Request-Response.
Test a New web service
You can test a New web service only in the Machine Learning Web Services portal.
In the Azure Machine Learning Web Services portal, click Test at the top of the page. The Test page opens and you can input data for the service. The input fields displayed correspond to the columns that appeared in the original credit risk dataset.
Enter a set of data and then click Test Request-Response.
The results of the test are displayed on the right-hand side of the page in the output column.

11. Manage and Access the web service
Manage Web Services
Sign in to the Microsoft Azure Machine Learning Web Services portal
Click Web services
Click your web service
Click the Dashboard
Access Web Services
Request/Response
Batch Execution
Request/Response - The user sends one or more rows of cdata to the service by using an HTTP protocol, and the service responds with one or more sets of results.
ii) Batch Execution - The user stores one or more rows of credit data in an Azure blob and then sends the blob location to the service. The service scores all the rows of data in the input blob, stores the results in another blob, and returns the URL of that container.
After a web service is deployed, a default endpoint is created for that service. The default endpoint can be called by using its API key.

12. References
Infographic of machine learning basics with links to algorithm examples
How to choose algorithms for Microsoft Azure Machine Learning.
A-Z list of Machine Learning Studio modules in Machine Learning Studio
How to consume an Azure Machine Learning Web service
Import training data into Machine Learning Studio
Extend your experiment with R and Execute Python machine learning scripts in Azure Machine Learning Studio
FAQs