Data science, machine learning, and analytics have re-defined how we look at the world. The R community plays a vital role in that transformation and the R language continues to be the de-facto choice for statistical computing, data analysis, and many machine learning scenarios. The importance of R was first recognized by the SQL Server team back in 2016 with the launch of SQL ML Services and R Server. Over the years we have added Python to SQL ML Services in 2017 and Java support through our language extensions in 2019. Earlier this year we also announced the general availability of SQL ML Services into Azure SQL Managed Instance.

Machine Learning Services is a feature of Azure SQL Managed Instance limited preview that provides in-database machine learning, supporting both Python and R scripts. The feature includes Microsoft Python and R packages for high-performance predictive analytics and machine learning. The relational data can be used in scripts through stored procedures, T-SQL script containing Python or R statements, or Python or R code containing T-SQL. Please email me at mawalli@microsoft.com if you have additional questions about this announcement. You must be a registered user to add a comment.

How does Azure Cosmos DB work under the hood?

There has been a lot of interest in the analytics community in visualizing the output of an Azure Machine Learning model inside Power BI. To add to the challenge, it would also be great to operationalize Azure ML models through the Power BI service. Imagine if you could have Power BI regularly bring in the latest output of your fraud model or the sentiment for recent Tweets about your products. The following tutorial will outline a proposed approach for doing just that. For the purpose of this tutorial we will assume your data is sitting inside an Azure SQL database.

There has been a lot of interest in the analytics community in visualizing the output of an Azure Machine Learning model inside Power BI. To add to the challenge, it would also be great to operationalize Azure ML models through the Power BI service. Imagine if you could have Power BI regularly bring in the latest output of your fraud model or the sentiment for recent Tweets about your products. The following tutorial will outline a proposed approach for doing just that. For the purpose of this tutorial we will assume your data is sitting inside an Azure SQL database.