Kubernetes Installation and Configuration Fundamentals

Course Overview

This course, Kubernetes Installation and Configuration Fundamentals, provides learners with the foundational knowledge needed to install, configure and deploy applications in a Kubernetes cluster. Learners will gain an understanding of Kubernetes' architecture, and how to install and configure a Kubernetes cluster on-premises with kubeadm, and in cloud scenarios such as Azure Kubernetes Services and Google Kubernetes Engine. Additionally, learners will explore how to interact with their cluster, deploying Pods, Services, and Deployments. By the end of the course, learners will have the skills and knowledge of Kubernetes needed to administer a Kubernetes cluster and deploy applications in Kubernetes. Software required: Linux.

[Applications]
After completing this course, participants should be able to apply their knowledge of Kubernetes Installation and Configuration Fundamentals to install, configure, and deploy applications in a Kubernetes cluster. They should also be able to interact with the cluster, deploying Pods, Services, and Deployments. Additionally, they should be able to perform installations on-premises with kubeadm, and in cloud scenarios such as Azure Kubernetes Services and Google Kubernetes Engine.

[Career Paths]
1. Kubernetes Administrator: Kubernetes Administrators are responsible for the installation, configuration, and maintenance of Kubernetes clusters. They must have a deep understanding of the Kubernetes architecture and be able to troubleshoot and debug any issues that arise. As Kubernetes becomes more popular, the demand for Kubernetes Administrators is increasing, making this a great career path for those interested in the field.

2. Kubernetes Developer: Kubernetes Developers are responsible for developing applications that run on Kubernetes clusters. They must have a deep understanding of the Kubernetes architecture and be able to write code that is optimized for Kubernetes. As Kubernetes becomes more popular, the demand for Kubernetes Developers is increasing, making this a great career path for those interested in the field.

3. Cloud Engineer: Cloud Engineers are responsible for the design, implementation, and maintenance of cloud-based systems. They must have a deep understanding of cloud computing technologies, such as Kubernetes, and be able to design and implement cloud-based solutions. As cloud computing becomes more popular, the demand for Cloud Engineers is increasing, making this a great career path for those interested in the field.

4. DevOps Engineer: DevOps Engineers are responsible for the automation of software development and deployment processes. They must have a deep understanding of DevOps tools and processes, such as Kubernetes, and be able to design and implement automated solutions. As DevOps becomes more popular, the demand for DevOps Engineers is increasing, making this a great career path for those interested in the field.

[Education Paths]
1. Bachelor of Science in Computer Science: This degree path provides students with a comprehensive understanding of computer science fundamentals, including programming, software engineering, and computer architecture. Students will also learn about the latest technologies and trends in the field, such as cloud computing, artificial intelligence, and machine learning.

2. Master of Science in Cloud Computing: This degree path focuses on the development and deployment of cloud-based applications and services. Students will learn about cloud computing architectures, cloud security, and cloud-native development. They will also gain experience in developing and deploying applications on cloud platforms such as Amazon Web Services, Microsoft Azure, and Google Cloud Platform.

3. Master of Science in Artificial Intelligence: This degree path focuses on the development of intelligent systems and applications. Students will learn about machine learning, natural language processing, computer vision, and robotics. They will also gain experience in developing and deploying AI-based applications and services.

4. Master of Science in Data Science: This degree path focuses on the analysis and interpretation of large datasets. Students will learn about data mining, data visualization, and predictive analytics. They will also gain experience in developing and deploying data-driven applications and services.