Azure Machine Learning & ML Studio

Machine Learning with simplicity and power of cloud

Vinnie SainiData & AI Solution Architectvasaini@Microsoft.com

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

Azure ML Architecture

Data

Tables,

Hadoop (HDInsight),

Relational DB(Azure SQL)

Predictive Model

ML Studio

Operational Web API

Clients Interface

• Machine learning workspace

• Dataset format

• Upload data

• Prepare data

• Define features

Create an experiment

• Choose and apply Learning Algorithms

• Train and Evaluate

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

Build a Data Science experiment in ML Studio

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.

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

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.

ML Studio Demo

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.

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

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