Build Your Own Text Classification Model

In a support center, text needs to be quickly routed to the correct person. Chatbots need to turn text into different scenarios to be addressed. Sentiment analysis determines whether text is positive, negative, or neutral. In all three of these examples, machine learning models can help. The Text Classification solution on the Azure AI Gallery…

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Getting Started with Machine Learning Services in SQL Server

I would like to introduce you to some simple videos that will help you get started with Machine Learning Services in SQL Server. The videos below cover how to install and enable Machine Learning Services. After completing the instructions found here, you will be ready to execute R and Python in SQL Server. I summarized…

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On-Prem or On the Cloud

Learn more about SQL Server Machine Learning Services in the Azure AI Gallery If you store your data in SQL Server 2016 or above, you should know about SQL Server Machine Learning Services (ML Services). Instead of moving data out of SQL to perform analytics, ML Services allows you to move the analytics (in R…

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Configuring Microsoft Machine Learning Server 9.3 to Operationalize Analytics using ARM Templates

Microsoft Machine Learning Server 9.3 released today. Operationalization refers to the process of deploying R and Python models to Machine Learning Server in the form of web services and the subsequent consumption of these services within client applications to deliver business results. We have now introduced Command Line Interface (CLI) for Machine Learning Server operationalization, making configuration both easy and…


How to use Tableau with SQL Server Machine Learning Services with R and Python

This post is co-authored by Bora Beran, Principal Product Manager at Tableau Software, Bharath Sankaranarayan, Principal Program Manager and Gil Isaacs, Senior Software Engineer from Microsoft Corporation In this post, I’ll be sharing how Tableau and SQL Server Machine Learning Services work seamlessly leveraging the power of R and Python. We like to think of…


Getting Started with Python Web Services using Machine Learning Server

Machine Learning Server 9.2 adds support for the full data science lifecycle of your Python-based analytics. In this article, we will go through step by step details of implementing data science lifecycle using Python. These steps include : Creating a VM configured as One-Box [using ARM Templates] Developing python models [using revoscalepy, microsoftml packages in any IDE]…


Configuring Microsoft Machine Learning Server to Operationalize Analytics using ARM Templates

To benefit from Machine Learning Server’s web service deployment and remote execution features, you must first configure the server after installation to act as a deployment server and host analytic web services. The installation process is already taken care by using these Azure Marketplace Images (which come with Machine Learning Server pre-installed): Data Science Virtual…


1 Million predictions/sec with Machine Learning Server web service

Microsoft Machine Learning Server Operationalization allows users to publish their R/Python based models and scripts as “Web-Services” and consume them from a variety of client applications in a scalable, fast, secure and reliable way. In this blog, I want to demonstrate how you can score > 1 Million predictions per second with the ‘Realtime’ web-services. Realtime web services…

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Amazon’s AWS Lambda and Alexa Skill with Microsoft Machine Learning Server Operationalization

In my previous blog, I talked about creating Enterprise-friendly Java clients for Machine Learning Server web-services. In this blog, we will see how easily we can extend this Java client to (1) work with  Amazon’s AWS Lambda function and (2) call this lambda function from a Amazon’s Alexa skill.   For this demo, I will assume that you have published…

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Host chat-bots with Microsoft Machine Learning Server Operationalization

Microsoft Machine Learning Server Operationalization allows users to create remote R or Python sessions, create Machine Learning models using their favorite R/Python packages and publish them as ‘Web-services’. While web-services are perfect for prediction/scoring scenarios, they are inherently stateless in nature and hence may not be suited for a ‘chat-bot’ like use-case which are often stateful. In this blog,…

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