Azure Machine Learning & ML Studio Machine Learning with simplicity and power of cloud Vinnie Saini Data & AI Solution Architect vasaini@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 Operational Web API Predictive Model Data Tables, Hadoop (HDInsight), Relational DB(Azure SQL) Predictive Model ML Studio Operational Web API Clients Interface

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.

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

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.

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. 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.

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. 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.

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.

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