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Support for Windows-based containers in Windows Server 2016 and Windows 10.

Microsoft introduced support for Windows-based containers in Windows Server 2016 and Windows 10.
With this, you can now take an existing Windows application, containerize it using Docker, and run it as an isolated container on Windows. Microsoft supports two flavors of Windows containers: Windows Server and Hyper-V.
You can build Windows containers on either the microsoft/windowsservercore and microsoft/nanoserver base images. You can read more about Windows containers in the Microsoft Windows containers documentation.
Google Cloud provides container-optimized VM images that you can use to run containers on Compute Engine.

Here is why when people discuss containers, they usually mean Linux-based containers

Container virtualization is a rapidly evolving technology that can simplify how you deploy and manage distributed applications. When people discuss containers, they usually mean Linux-based containers. This makes sense, because native Linux kernel features like cgroups introduced the idea of resource isolation, eventually leading to containers as we know them today. For a long time, you could only containerize Linux processes, but Microsoft introduced support for Windows-based containers in Windows Server 2016 and Windows 10.

Here is why Google Cloud is using P4Runtime

For those who are unfamiliar, Google Fellow Data networks are difficult to design, build and manage, and often don’t work as well as. Almost ten years ago, Google took steps to address this by adopting software-defined networking (SDN) as the basis for their network architecture. SDN allowed the company to program their networks with software running on standard servers and became a fundamental component of their largest systems. On the same momentum, the next step with P4Runtime means that, Google Cloud is using P4Runtime to build smart networks.

(AI) Artifcial Intelligence Performance

By using machine learning on POWER9 with NVIDIA Tesla V100 GPUs, one can observe that, in a newly published benchmark by IBM Research, Snap Machine Learning (AI technology) can be used to train machine learning models for massive data sets from financial records to weather forecasting to online marketing. The result for customers is lower cloud costs and faster time to insight.

Web content by language, 2018

Web content by language, 2018.

English: 51.2%
Russian: 6.8%
German: 5.6%
Japanese: 5.5%
Spanish: 5.1%
French: 4.1%
Portuguese: 2.6%
Italian: 2.4%
Chinese: 2.1%
Polish: 1.7%
Persian: 1.7%
Turkish: 1.4%
Dutch: 1.3%
Korean: 1.0%
Czech: 0.9%
Arabic: 0.7%

(W3Techs)

Web content by language, 2018

Web content by language, 2018.

English: 51.2%
Russian: 6.8%
German: 5.6%
Japanese: 5.5%
Spanish: 5.1%
French: 4.1%
Portuguese: 2.6%
Italian: 2.4%
Chinese: 2.1%
Polish: 1.7%
Persian: 1.7%
Turkish: 1.4%
Dutch: 1.3%
Korean: 1.0%
Czech: 0.9%
Arabic: 0.7%

(W3Techs)

In 2020, AI will create 2.3 million jobs while eliminating 1.8 million jobs.

Gartner: In 2020, AI will create 2.3 million jobs while eliminating 1.8 million jobs. AI will improve the productivity of many jobs, and, used creatively, it has the potential to enrich people's careers, re-imagine old tasks and create new industries.