Kubernetes has revolutionized the way applications are deployed, scaled, and managed in cloud environments. It was initially designed by Google, and its primary function was to manage containerized applications across a single cluster.

The rapid advancements in generative AI have propelled machine learning and artificial intelligence from academia to real-world applications. Despite this progress, organizations face a persistent bottleneck in deploying models effectively, which impacts their ability to optimize impact, performance, and cost-effectiveness.

AI pipelines streamline the process of building and deploying machine learning (ML) workflows, enhancing scalability and portability. Kubernetes plays a pivotal role in managing these pipelines, allowing for effortless deployment, scaling, and operations of application containers across clusters of hosts.

An increasing number of GitOps teams are recognizing the need to construct their own automated Kubernetes CI/CD platform. This trend arises from the desire to faithfully replicate the production environment, create customized app testing workflows, gain better control over resources, achieve better security, and manage costs effectively.

When you dive into the world of Kubernetes, you’ll quickly encounter “deployments.” At their core, Kubernetes deployments offer a way to manage and maintain your application’s desired state.

Kubernetes has revolutionized the way applications are deployed and scaled, and Kubernetes services are a fundamental component of its networking model. Kubernetes services provide a stable endpoint for communication by abstracting away the details of pod IP addresses.

Multi-tenant Kubernetes environments allow a single cluster to be shared by several teams, applications, customers, or environments. Workloads are isolated from each other; they reside in individual slices of the cluster.

Kubernetes requests and limits define the amount of CPU and memory that pods and containers are able to consume. Setting appropriate requests and limits on your pods prevents resource contention and helps ensure efficient scheduling to your cluster’s nodes.

If you are running a Kubernetes cluster, you probably need an ingress controller. An ingress controller is a Kubernetes resource responsible for overseeing external access to the services within your cluster.

When dealing with Kubernetes workloads, external access to applications running in the cluster is often needed. Kubernetes gives you a few ways to achieve this. You could manually configure a NodePort or LoadBalancer service.

Ingress is an essential Kubernetes API object that manages external access to services within a cluster, typically via HTTP, and is aimed at providing routing configurations.

In recent years, Kubernetes has become the standard in container orchestration. Originally designed by Google, it outran its competition. It’s much more popular than OpenShift Container Platform and Apache Mesos.