KServe Fundamentals: Serving ML Models on Kubernetes

The KServe Fundamentals: Serving ML Models on Kubernetes course is designed to help engineers and AI practitioners build a strong foundation in cloud-native model serving using KServe. Tailored for DevOps engineers, MLOps practitioners, platform engineers, Kubernetes administrators, and AI engineers, this course introduces the core concepts and practical workflows required to deploy, manage, and troubleshoot machine learning inference workloads on Kubernetes. Through conceptual lessons, guided demonstrations, and hands-on exercises, you’ll learn how modern AI and machine learning models are deployed and served in production environments using KServe.

Throughout the course, you’ll explore KServe architecture, deploy AI workloads using InferenceService resources, and learn how to expose and interact with model endpoints. You’ll deploy a quantized Qwen large language model, serve a predictive text classification model, and understand the differences between generative and predictive inference workloads. The course also covers troubleshooting fundamentals, including reading InferenceService status and diagnosing failed deployments, giving you the practical skills needed to confidently serve and manage ML models in Kubernetes environments.

Course Modules & Learning Outcomes

Foundations

The Foundations module introduces the fundamentals of model serving and the architecture of KServe. You’ll learn how KServe integrates with Kubernetes, understand its key components, and perform a complete KServe installation. By the end of this module, you’ll have a clear understanding of the building blocks required for serving machine learning models in Kubernetes environments.

Serving a Generative Model

This module focuses on deploying and serving generative AI workloads using KServe. You’ll work with InferenceService manifests, deploy a quantized Qwen model, and learn how to interact with model endpoints using inference requests. Through practical demonstrations and hands-on exercises, you’ll gain experience deploying and consuming generative AI models in production-style environments.

Serving a Predictive Model

Explore how predictive machine learning model serving differs from generative AI workloads. In this module, you’ll deploy and test a text classification model while learning the adjustments required for predictive inference services. This section helps build a broader understanding of ML serving use cases and deployment patterns.

Troubleshooting Basics

This module introduces essential troubleshooting techniques for KServe deployments. You’ll learn how to inspect InferenceService status conditions, identify deployment issues, and diagnose broken inference services. These operational skills are critical for maintaining reliable AI workloads in Kubernetes environments.

Course Features

• Comprehensive coverage of KServe fundamentals, model serving concepts, Kubernetes integration, and inference workflows.
• Hands-on labs and guided demonstrations to help you deploy and interact with real AI and machine learning models.
• Knowledge-check quizzes at the end of each module to reinforce key concepts and measure your understanding.

Who Should Enroll?

• DevOps engineers interested in AI infrastructure and MLOps practices.
• Platform engineers managing Kubernetes-based AI workloads.
• AI and machine learning engineers looking to deploy models in production environments.
• Kubernetes administrators expanding into cloud-native AI serving.
• Anyone interested in learning how modern ML models are deployed and served using KServe.

Build the foundational skills required to serve, manage, and troubleshoot machine learning models on Kubernetes with KServe through practical demonstrations, hands-on deployments, and real-world inference workflows.