AI News, SQL Server as a Machine Learning Model Management System

SQL Server as a Machine Learning Model Management System

If you are a data scientist, business analyst or a machine learning engineer, you need model management – a system that manages and orchestrates the entire lifecycle of your learning model.

As a result, it is pretty agnostic to the analytics engines that were used to build models, thus making it a pretty good model management tool for not only R models (because R is now built-in into SQL Server 2016) but for other runtimes as well.

While there are existing tools that provide some capabilities for managing models and deployment, using SQL Server keeps the models “close” to data, thus leveraging all the capabilities of a Management System for Data to be now nearly seamlessly transferrable to machine learning models (see Figure 2).

In general, data scientists collect the data they are interested in, prepare and stage the data, apply different machine learning techniques to find a best-of-class model, and continually tweak the parameters of the algorithm to refine the outcomes.

For example, a data scientist must code the model, select parameters and a runtime environment, train the model on batch data, and monitor the process to troubleshoot errors that might occur.

As models are built and trained by many data scientists, the same algorithms may be used to build similar models, particularly if a certain set of algorithms is common for a business’s use cases.

Specifically, you can use SQL Server 2016 for the following: Here is the list of specific capabilities that makes the above possible: In summary, SQL Server delivers the top-notch data management with performance, security, availability, and scalability built into the solution.

As companies rely more heavily on data analytics and machine learning, the ability to manage, train, deploy and share models that turn analytics into action-oriented outcomes is essential.

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