Microsoft explains how machine learning improves the Windows 10 update experience

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Microsoft changed update testing significantly in recent time. Previously, it relied much on teams of testers and "real" hardware tests, but that shifted to test automation, tests on Windows Insider computer systems, and machine learning.

Machine learning helped improve the Windows 10 update experience according to Microsoft. A new blog post on the company's Tech Community website reveals details about the use of machine learning in regards to the creation and release of updates for Windows.

The long article is quite technical in nature but it might be enough to read the opening paragraph to understand how machine learning is used by Microsoft when it comes to updates for Windows.

Machine learning helps us detect potential issues more quickly and helps us decide the best time to update each PC once a new version of Windows is available.

In short, it is used by Microsoft to evaluate updates and to help with roll outs of feature updates. The article focuses on the use of machine learning to assist in the rollout of feature updates for Windows 10.

Microsoft started to use machine learning in broad scale when it released the April 2018 Update for Windows 10. Machine learning was used to determine the release quality by monitoring six "core areas of PC health" including PC reliability.

The number of areas increased to 35 when Microsoft released the May 2019 Update in 2019 and Microsoft plans to extend the coverage further for future updates.

Microsoft notes that PCs that are selected by the company's Machine Learning algorithms "have a significantly better update experience". PCs selected by Machine Learning have "fewer than half the number of system-initiated uninstalls, half the number of kernel mode crashes, and five times fewer post-update driver issues".
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