Learn best practices and tools required to design and build an end-to-end machine learning platform.

Developers often struggle when first encountering the cloud. Learning about distributed systems, becoming familiar with technologies such as containers and functions, and knowing how to put everything together can be daunting. With this practical guide, you'll get up to speed on patterns for building cloud native applications and best practices for common tasks such as messaging, eventing, and DevOps. Authors Boris Scholl, Trent Swanson, and Peter Jausovec describe the architectural building blocks for a modern cloud native application. You'll learn how to use microservices, containers, serverless computing, storage types, portability, and functions.

Kubernetes is a distributed cluster technology that manages container-based systems in a declarative manner using an API. There are currently many learning resources to get started with the fundamentals of Kubernetes, but there is less information on how to manage Kubernetes infrastructure on an ongoing basis.

To keep pace with the ever-changing digital landscape, organizations are adopting open source and cloud native technologies at an incredible pace. But as the number of clusters and workloads grow, it can become increasingly difficult to know where clusters exist and how they are performing. And if multiple teams are provisioning and using clusters with different policies, roles, and configurations, you might as well jump ship. Because before you know it, you'll begin to experience cluster sprawl, and your multi-cluster operations will potentially capsize before you reach shore.

We provide you with best practices and pitfalls to avoid in order to stand up a production-ready Kubernetes environment.

The reality is, if your revenue and productivity is powered by cloud native applications, risk is not an acceptable option.