Machine learning plays a crucial role in modern SQL Server monitoring solutions by enabling predictive analytics, anomaly detection, and automated performance optimization.

But it was SQL Server’s new machine learning tools that grabbed my attention. Machine learning remains one of Microsoft’s big themes for 2017, and it’s an important segment of SQL Server 2017.

Automated machine learning can be used from SQL Server Machine Learning Services, python environments such as Jupyter notebooks and Azure notebooks, Azure Databricks, and Power BI. Starting in SQL ...

SQL Server approach to machine learning model management is an elegant solution. While there are existing tools that provide some capabilities for managing models and deployment, using SQL Server ...

Use dynamic management views (DMVs) to monitor the execution of external scripts (Python and R), resources used, diagnose problems, and tune performance in SQL Server Machine Learning Services. In ...

Over a month after SQL Server 2019 became generally available, Microsoft has now shined light on some more aspects of the improved machine learning capabilities offered with this release.

Microsoft says SQL Server 2019 now has deeper abilities for training machine learning models and more integration with Python and R. By Luke Jones December 18, 2019 5:08 pm CET ...

Installation guidance for SQL Server on Linux and verify the installation.. Check the SQL Server Linux repositories for the Python and R extensions. If you already configured source repositories for ...

SQL injection is the most common web application vulnerability. The vulnerability can be generated unintentionally by software developer during the development phase. To ensure that all secure coding ...

Machine learning has become a game-changer in various fields, and SQL Server monitoring is no exception. Monitoring solutions traditionally relied on predefined thresholds and rules to flag ...