Microsoft started using machine learning (ML) to manage the rollout of Windows 10 feature updates with the Windows 10 April 2018 Update (version 1803). In a new blog post by Microsoft's Archana Ramesh and Michael Stephenson, both data scientists for Microsoft Cloud and AI, the company outlines improvements made since then. Microsoft has been having a tough time recently with the quality of cumulative updates (CU) and feature updates for Windows 10. While the tech media tends to blow things out of proportion sometimes, I think it's fair to say that quality has taken a knock since internal testers were dismissed in favor of the Windows Insider Program. Biannual feature updates haven't been without their issues either. But because of the diversity of the Windows ecosystem, regardless of how much testing is done, there is always the potential for issues when making changes to a complex piece of software like Windows.

Microsoft has explained before that it uses PC "telemetry" information and machine learning to assess the readiness of PCs for Windows 10 feature upgrade releases, which arrive twice per year. In a blog post this week, Microsoft data scientists Archana Ramesh and Michael Stephenson expanded on how the company uses machine learning algorithms to ensure those feature upgrades are rolled out successfully at organizations. Background A Windows 10 feature upgrade essentially replaces the underlying operating system's bits with a new OS version. The process is called an "in-place upgrade." However, there are lots of potential issues that can arise with each OS upgrade, given the variations in hardware, drivers and software that exist, as well as flaws that may be present in the upgrade itself. Some Windows 10 upgrade releases have been minor disasters.

Last week at its Ignite Conference, Microsoft unveiled the preview version of Automated Machine Learning(ML), a component of Azure ML that allows non-data science experts to build machine learning pipelines. Microsoft's automated machine learning can be seen as their entrance in the popular Auto ML space which is quickly becoming one of the most active areas of research in the machine learning space. The work behind Microsoft's automated machine learning were outlined in a research paper published a few months ago. Building machine learning solutions in the real world is a never-ending cycle of different steps such as extracting features, identifying models, tuning hyperparameters, etc. Each one of these tasks requires specific expertise that results overwhelming to non-data scientists.

On any given night, 7 billion people might look into the sky and see things from a slightly different perspective. It's one world map of the night sky that encapsulates the way we all see it--subjectively from around the globe--through software. Technically, the map doesn't have 7 billion views from across the globe. Instead, it features eight different climate regions, sort of like a weather map might. So it has the polar sky, the arid sky, and the tropical monsoon sky, interspersed across the continents.