The 10 best Kubernetes courses in 2026

This course introduces you to container technologies and how they can be used to modernize your applications, as well as exploring how different AWS services can be used to manage and orchestrate those containers. Container technologies have existed for years, and are still gaining popularity. Two of the most prevalent options are Docker and Kubernetes - each with its own distinct set of features. Regardless of which technology you choose, one of the biggest challenges with containers is their orchestration. Unlike traditional, monolithic applications where you can only scale at a macro level

This course helps you assess your readiness and build practical skills before taking the real exam. You’ll get step-by-step instructions on using the sample exams and grading scripts, so you can make the most of your practice. The course includes two full-length practice exams that closely match the actual certification test. Practice solving real-world tasks under timed conditions, then review your answers with detailed instructor walkthroughs. This process will show you what you know and where you need to improve, so you can focus your study time effectively.

By the end of this Kubernetes Training, learners will be able to identify Kubernetes components, explain cluster architecture, configure clusters on AWS, set up environments with Kubeadm, and apply troubleshooting techniques using Minikube. This course is designed to move beyond theory, providing hands-on demonstrations that empower learners to create, manage, and scale containerized applications in both cloud and local environments. Learners will benefit from completing this course by gaining practical, job-ready skills essential for DevOps engineers, cloud administrators, and software devel

This course is designed for aspiring DevOps professionals and IT enthusiasts. Dive deep into the intricacies of services and networking, mastering the skills necessary to efficiently manage and optimize application traffic within your Kubernetes clusters. Through hands-on labs and expert-led lessons, you'll gain practical experience and a solid understanding of essential networking concepts, ensuring you're well-equipped to tackle real-world challenges.

In this course, you will embark on a transformative journey to master the foundational elements of Kubernetes, from understanding containers and container images to deploying your first application in a lab environment. This course is meticulously crafted to equip you with the essential skills and knowledge required to excel in Kubernetes Certified Application Developer (CKAD) topics. Through engaging lessons and hands-on labs, you'll gain practical experience and confidence to navigate the Kubernetes ecosystem with ease. Enroll today and take the first step towards becoming a Kubernetes expe

This is a self-paced lab that takes place in the Google Cloud console. Google Kubernetes Engine provides a managed environment for deploying, managing, and scaling your containerized applications using Google infrastructure. This hands-on lab shows you how deploy a containerized application with Kubernetes Engine.

Mastering Kubernetes is critical for modern cloud-native environments, and this course dives deep into securing clusters, managing workloads, and scaling applications efficiently. You will gain expertise in advanced scheduling, autoscaling, and traffic management, enabling resilient and high-performing deployments. Throughout the course, learners will explore practical techniques for securing Kubernetes environments, optimizing pod and node scaling, and implementing multi-cluster strategies. Hands-on exercises help translate complex concepts into actionable skills for real-world scenarios. W

This course provides an intermediate-level exploration of MLOps, focusing on how machine learning systems are scaled, productionized, and managed across feature engineering, training, orchestration, serving, and deployment. You will examine how modern ML solutions use feature stores, Kubernetes, Kubeflow, distributed training, advanced serving frameworks, progressive release strategies, and inference optimization techniques. Through hands-on demonstrations and practical exercises, you will gain experience building reliable and scalable workflows with industry-standard technologies such as Fe

Deploying machine learning models into production systems requires more than training a model—it requires reliable deployment, monitoring, and debugging practices. In this course, you'll learn how to deploy machine learning models as scalable services and maintain them within real software architectures. You’ll begin by learning how to package and deploy machine learning models using containerization and orchestration technologies. You’ll apply tools such as Docker and Kubernetes to manage application deployment and ensure that models run consistently across environments. Next, you’ll design

This course teaches how to deploy and monitor microservices in cloud-native environments using Kubernetes. Mastering these skills is critical for building scalable, observable, and maintainable distributed systems. Learners will deploy microservices to Kubernetes, use service meshes for observability, implement centralized logging with the EFK stack, and monitor system performance. These exercises give hands-on experience in real-world deployments. The course combines theoretical concepts with practical applications, covering Kubernetes features, native compiled Java microservices, and cross