Kubernetes

Kubernetes is an open-source platform that automates deploying, scaling, and managing containerized applications across a cluster of machines. The name comes from the Greek word for helmsman, which fits: Kubernetes acts as the pilot that keeps a fleet of containers running smoothly instead of you manually restarting crashed processes or routing traffic by hand. Google open-sourced the project in 2014, building on more than a decade of running production workloads internally, and it is now the second-largest open-source project in the world after Linux. For engineers running AI services or any application built from many moving parts, Kubernetes is the standard way to keep those parts running reliably in production.

How Kubernetes works

Docker packages an application into a container: code, dependencies, and runtime bundled into one unit that runs the same way on any machine (a container is a lightweight, isolated environment for running software, distinct from a full virtual machine). Kubernetes takes those containers and runs them across a cluster, a group of machines working together as one system. Here is how the core pieces fit together:

• Pods: The smallest deployable unit in Kubernetes. A pod wraps one or more containers that need to run together and share network and storage.
• Nodes: The individual machines, physical or virtual, that make up the cluster. Kubernetes schedules pods onto nodes based on available resources.
• Deployments: A declarative description of how many copies of a pod should be running. You tell Kubernetes the desired state, and it works continuously to match reality to that state.
• Services: A stable network address that routes traffic to the right pods, even as individual pods are created, destroyed, or moved between nodes.
• Horizontal Pod Autoscaler (HPA): A controller that adds or removes pod replicas automatically based on CPU usage, memory, or custom metrics you define.
• Control plane: The brain of the cluster. It watches the current state of everything, compares it to the desired state, and takes action, like restarting a crashed pod, whenever the two drift apart.

What you can build with Kubernetes

• Auto-scaling API backend: Deploy a REST or GraphQL API as a set of pods behind a Service, then let the Horizontal Pod Autoscaler add replicas automatically when traffic spikes and remove them when it settles down.
• Zero-downtime deployment pipeline: Use a rolling update strategy so new versions of your app roll out gradually, with health checks confirming each new pod is healthy before the old one is terminated.
• Multi-model AI inference cluster: Run several machine learning models as separate deployments, each with its own resource limits and autoscaling rules, so a spike in demand for one model does not starve the others.
• Hybrid or multi-cloud application: Describe your entire application as Kubernetes manifests, then deploy the identical setup to AWS, Google Cloud, or your own data center without rewriting deployment scripts.
• Batch and scheduled job processor: Use Kubernetes Jobs and CronJobs to run data pipelines, model retraining, or nightly reports on a schedule, with the cluster handling retries if a job fails.