> ## Documentation Index > Fetch the complete documentation index at: https://notes.kodekloud.com/llms.txt > Use this file to discover all available pages before exploring further. # Section Introduction > How to productionize machine learning using Kubernetes with KServe for scalable model serving and Katib for automated hyperparameter optimization Most machine learning work starts and often ends inside a notebook: train a model, check accuracy, maybe export a file — then consider the task done. Production machine learning is different. When a model must serve predictions to applications, handle traffic, scale automatically, and continuously improve, it becomes an infrastructure problem. That shift—from a single notebook to a live, reliable service—is the focus of this section. Operationalizing ML introduces new engineering questions: * How do we expose models reliably through APIs? * How do we scale inference for steady, bursty, or unpredictable traffic patterns? * How do we monitor and continuously improve model performance in production? * How do we automate experimentation (hyperparameter search) instead of manually tuning parameters? Kubernetes provides the orchestration layer that addresses many of these questions. On top of Kubernetes, ML-focused systems handle model serving and autoscaling: * KServe simplifies deploying models as scalable, production-ready inference services on Kubernetes. * Knative provides serverless primitives for autoscaling and event-driven workloads. An infographic slide titled "Production Machine Learning Requires New Systems" comparing naive model duplication (where copies fail randomly) on the left with Kubernetes-managed scaling on the right, showing multiple pods labeled Pod 1–4 all running. The image illustrates orchestration moving from fragile copies to a Kubernetes control plane managing healthy pods.

An infographic slide titled "Production Machine Learning Requires New Systems" comparing naive model duplication (where copies fail randomly) on the left with Kubernetes-managed scaling on the right, showing multiple pods labeled Pod 1–4 all running. The image illustrates orchestration moving from fragile copies to a Kubernetes control plane managing healthy pods.

This section concentrates on two core, complementary systems within the modern MLOps stack: * [KServe](https://learn.kodekloud.com/user/courses/kserve-fundamentals-serving-ml-models-on-kubernetes) — a Kubernetes-native model serving platform that exposes models as scalable HTTP/gRPC inference APIs and integrates with autoscaling and Kubernetes networking. * [Katib](https://learn.kodekloud.com/user/courses/kubeflow) — an automated hyperparameter optimization framework that runs experiments to discover better-performing model configurations. A slide titled "The Two Core Systems in This Section" showing two cards: KServe — a model serving platform for deploying models as scalable APIs with Kubernetes-native inference; and Katib — a hyperparameter optimization platform that automates experimentation to find better-performing models.

A slide titled "The Two Core Systems in This Section" showing two cards: KServe — a model serving platform for deploying models as scalable APIs with Kubernetes-native inference; and Katib — a hyperparameter optimization platform that automates experimentation to find better-performing models.

Together, KServe and Katib create a production-style ML workflow: automate experiments to find strong models, then serve the selected model reliably at scale. What to expect in this section: * A practical end-to-end example using the Iris dataset. * Training a Random Forest model and evaluating how hyperparameters affect performance. * Using Katib to automate hyperparameter search and run reproducible experiments. * Deploying the optimized model with KServe and exposing it as a live inference API on Kubernetes. Quick comparison | Component | Primary role | Typical outcome | | - | - | - | | KServe | Model serving / inference | A production-ready inference endpoint (HTTP/gRPC) with autoscaling and observability | | Katib | Hyperparameter optimization | Automated experiments that return optimal hyperparameters and improved model metrics | Before you begin: ensure you have access to a Kubernetes cluster and a working `kubectl` context. Installing KServe and Katib requires cluster admin privileges. For guided installs and permissions, see the linked course pages above. By the end of this section you will understand: * Why ML in production is primarily an infrastructure problem. * How Kubernetes enables reliable, scalable model serving. * How Katib automates experimentation so teams can find better model configurations faster. * How to combine these tools into repeatable, production-ready MLOps workflows. Links and references * [Kubernetes Basics](https://kubernetes.io/docs/concepts/overview/what-is-kubernetes/) * [KServe course (KodeKloud)](https://learn.kodekloud.com/user/courses/kserve-fundamentals-serving-ml-models-on-kubernetes) * [Katib / Kubeflow course (KodeKloud)](https://learn.kodekloud.com/user/courses/kubeflow) * [Knative (serverless on Kubernetes)](https://knative.dev) * [Fundamentals of MLOps (KodeKloud)](https://learn.kodekloud.com/user/courses/fundamentals-of-mlops) This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.