While service providers are quickly ramping up their capabilities to orchestrate Virtualized Network Functions and SDN components, many are starting to realize that work gets them only part way to the …

Jasmin Nakic and Samir Pilipovic examine the application of a linear regression predictive model on time series performance data, discussing and evaluating different models to find the optimal choice for a given dataset. All steps will be supported with Python-based scripts so that you can easily implement similar models on your own data.

Kubernetes provides the ability to extend the platform with your own custom types and controllers. We will walk through a tutorial to write a custom controller, also known as an operator. Patterns wil …

Keeping up to date with the latest web performance trends, features, and techniques can be time consuming and hard. Gareth Hughes and Michael Gooding guide you through the top five performance techniques for today's modern browsers and provide the tools and knowledge you need to implement them today.

Can a small team operating 2000 CPUs, escape the glorified bash infighting of Configuration Management to homestead the scalable compute plains of Kubernetes? A journey of transitioning from Googles …

Join in to learn how to do scalable, end-to-end data science in R on single machines as well as on Spark clusters. You'll be assigned an individual Spark cluster with all contents preloaded and software installed and use it to gain experience building, operationalizing, and consuming machine-learning models using distributed functions in R.