Course
Introduction to Kubernetes
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Kubernetes is a solution that orchestrates and maintains containerized applications effectively. Since its introduction in 2014 by Google, this open-source system has developed into the tool of choice for managing all kinds of distributed workloads. It is an essential part of today's DevOps world, helping to develop and deploy scalable applications and workflows of any kind.
In this course, you'll become a Kubernetes champ with hands-on experience using Kubernetes on the command line. You will master kubectl, the Kubernetes command line tool, and use it to interact with real Kubernetes clusters. You will learn the fundamentals of Kubernetes and understand how to deploy and orchestrate containers using Manifests and kubectl instructions.
After an introduction to Kubernetes and its relation to modern software architecture and cloud-nativeness, you will learn your first Kubernetes objects and how to apply them like a pro.
As you progress, you'll learn about more advanced concepts like stateful applications, storage, and networking. To wrap up, you will use these concepts in an exemplary MLOps workflow.
Prerequisites
Introduction to DockerIntroduction to Kubernetes (K8s)
Deploying Software on K8s
Data Engineering and MLOps
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FAQs
What prerequisites do I need for this Kubernetes course?
You should have experience with Docker, basic shell commands, and containerization concepts. Prior courses on Docker, Shell, and data engineering are recommended.
Will I work with real Kubernetes clusters?
Yes. The course provides hands-on experience using kubectl to interact with real Kubernetes clusters directly from the command line.
What is kubectl and why does it matter?
kubectl is the command line tool for interacting with Kubernetes clusters. You will use it throughout the course to deploy, manage, and orchestrate containers.
Does this course cover Kubernetes for data engineering or MLOps?
Yes. The final chapter covers how Kubernetes is used in data engineering and MLOps workflows to deploy and manage scalable data pipelines and models.
How is the course structured?
The course has 3 chapters and 37 exercises. It covers Kubernetes architecture, deploying software with kubectl, and data engineering and MLOps use cases.
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