10 years of OpenStack, Kubernetes at the edge, and more industry trends

10 years of OpenStack, Kubernetes at the edge, and more industry trends

As part of my role as a principal communication strategist at an enterprise software company with an open source development model, I publish a regular update about open source community, market, and industry trends. Here are some of my and their favorite articles from that update.

As part of my role as a principal communication strategist at an enterprise software company with an open source development model, I publish a regular update about open source community, market, and industry trends. Here are some of my and their favorite articles from that update.

OpenStack 10 Years: A revolution for telco

On the 10 years’ birthday of OpenStack, we would like to thank this community for continuously being active and productive and evolving so fast to fulfill the requirement of telcos in such a short time. As telco operators, we see OpenStack as the key for multi-vendor cloud. We would like to continuously contribute and support this community, to make sure a sustainable open-source community is always behind to act as the de-facto standard for our cloud.

The impact: First off: 10 years! I remember hosting a "5 year OpenStack birthday party" what seemed like only yesterday. Secondly, telcos had a lot to do with the maturity of OpenStack into what it is today.

Kubernetes could be the one to make the Internet of Things (IoT) reach its potential

As much as IoT and 5G fit into the future of digital communications, Kubernetes seems poised to take an equally important role in how those environments are run and managed. With Gartner estimating that by 2025, more than 75 percent of enterprise-generated data could be created and processed outside of traditional data centers and clouds, the future is bright for an orchestration system like Kubernetes that has already proven to be the best tool for that job.

The impact: The enterprise usage of Kubernetes is skyrocketing, so data generation is already massive. Three times as much enterprise data coming from and processed at the edge than in the cloud or data center? That's hard... to compute...

Deep learning frameworks compared: MxNet vs TensorFlow vs DL4j vs PyTorch

Without the right framework, constructing quality neural networks can be hard. With the right framework, you only have to worry about getting your hands on the right data.

The impact: These frameworks massively broaden the accessibility and approachability of machine learning. Combined with some of the open data sets out there, a novel insight or new career could be just around the corner.

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