Final Words

Software developers can attest that containers have offered flexibility to run cloud native applications on virtual and physical infrastructure. Containers package the services comprising of applications makes them portable across varying computer environments for use in development, testing, and production. For instance, it is possible to ramp application instances to match spikes quickly with containers. Because containers draw resources from the host OS, they tend to be much lighter in weight compared to VMs. This means that, containers make utmost use of the underlying server infrastructure.

Being an open source system for scaling, managing, and deploying containerized applications, Kubernetes handles scheduling container tasks onto computer clusters and further manages the arising workloads with the aim of ensuring they operate as the user envisioned. This all further means that, Kubernetes combines software development and operations through design, as opposed to bolting on operations as an afterthought. In addition, Kubernetes supports the order of magnitude increase, in regards to operations of modern software systems through the use of declarative and infrastructure agnostic constructs that describe how applications interact, how they are managed, as well as how they are created.

Developers are called upon to develop applications that can operate across multiple operating environments like virtualized private clouds, dedicated on-prem servers, as well as public clouds like Azure and AWS. Before, applications and their supporting tooling were closely tied to underlying infrastructure; this made it costly to apply other applicable deployment models, despite the advantages they offered. Applications were highly dependent on specific environments, as per different respects such as: performance issues that were associated to pre-determined network architecture and adherence to specific constructs based on cloud providers. Note that PaaS tries to resolve such issues, but there is a caveat: strict requirements are imposed in areas like application frameworks and programming languages. As such, PaaS is not preferred by many development teams. Through offering core container capabilities without imposing various restrictions, Kubernetes does away with the infrastructure lock-in, which is achieved through a combination of features such as services and pods within the Kubernetes platform.

Evidently, from the information in this book, Kubernetes creates a breakthrough for DevOps by allowing teams to keep up with the modern software development requirements. When Kubernetes is absent, teams are forced to script their unique software deployment, update, and scale workflows. This explains why some organizations prefer to hire large IT teams who can handle such tasks alone. Kubernetes is designed to ensure that users derive maximum utility from applicable containers, enabling them to build cloud native applications that can run independently and anywhere. This makes Kubernetes an effective and efficient application development and operation model that has been missing on many application teams.

Kubernetes is not a traditional PaaS system because it operates at the container level, as opposed to the hardware level. Instead, it offers some applicable features that are common to what PaaS offers, such as: load balancing monitoring, logging, and deployment. It is important to note that such default settings are pluggable and optional, since Kubernetes is not monolithic. What is important is that Kubernetes offers the ideal building blocks for creating developer platforms while preserving flexibility and user choices where necessary. It is also important to note that Kubernetes is not just a mere orchestration system; it eliminates the need for orchestration. This is because it contains a set of independent and composable control processes, triggering the system to support the desired state.

Kubernetes has a lot to offer, especially to data scientists who are interested in the development of techniques that can solve critical business challenges through machine learning. Most importantly, it offers the end users more advantages because machine learning represents varying workloads that can train models and offer insights. Machine learning is applied in a production as a crucial part of an intelligent application.