Argo Workflows on Kubernetes - Core Concepts

Course Overview

Course Overview:
This course introduces the core concepts of Argo Workflows and how to use them to orchestrate Kubernetes-native workflows. It covers the installation of a minikube cluster with Argo Workflows on your local machine, how to communicate with the Argo server using the kubectl CLI and the Argo Server UI, and how to create workflows using the core concepts of Argo Workflows.

Possible Development Directions:
This course is an introductory course to the full course Hands-On Guide to Argo Workflows on Kubernetes. After completing this course, users can further develop their knowledge by exploring the full course. The full course covers topics such as creating templates and cron workflows, using different artifact repositories, and managing thousands of parallel pods and workflows within a Kubernetes cluster.

Related Learning Suggestions:
In addition to the full course, users can also explore other related topics such as machine learning, ETL, Batch - and data processing, and CI / CD. They can also join the global Argo Workflows community to learn more about the project and get support from other users. Furthermore, users can also explore other open source projects of the Cloud Native Computing Foundation.

[Applications]
You will also be able to apply the knowledge gained in this course to the full course Hands-On Guide to Argo Workflows on Kubernetes. This will enable you to create more complex workflows and use the advanced features of Argo Workflows.

[Career Paths]
1. Kubernetes Engineer: Kubernetes Engineers are responsible for managing and maintaining Kubernetes clusters. They are responsible for deploying and configuring applications on Kubernetes clusters, as well as troubleshooting and resolving any issues that arise. They must also be knowledgeable about Argo Workflows and be able to use them to orchestrate jobs in Kubernetes. The demand for Kubernetes Engineers is growing rapidly as more organizations are adopting Kubernetes and Argo Workflows.

2. DevOps Engineer: DevOps Engineers are responsible for automating the deployment and management of applications on Kubernetes clusters. They must be knowledgeable about Argo Workflows and be able to use them to orchestrate jobs in Kubernetes. They must also be able to troubleshoot and resolve any issues that arise. The demand for DevOps Engineers is growing rapidly as more organizations are adopting Kubernetes and Argo Workflows.

3. Data Engineer: Data Engineers are responsible for designing, building, and maintaining data pipelines. They must be knowledgeable about Argo Workflows and be able to use them to orchestrate jobs in Kubernetes. They must also be able to troubleshoot and resolve any issues that arise. The demand for Data Engineers is growing rapidly as more organizations are adopting Kubernetes and Argo Workflows for data processing and ETL.

4. Machine Learning Engineer: Machine Learning Engineers are responsible for designing, building, and deploying machine learning models. They must be knowledgeable about Argo Workflows and be able to use them to orchestrate jobs in Kubernetes. They must also be able to troubleshoot and resolve any issues that arise. The demand for Machine Learning Engineers is growing rapidly as more organizations are adopting Kubernetes and Argo Workflows for machine learning.

[Education Paths]
Recommended Degree 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. It also covers topics such as artificial intelligence, machine learning, and data science. This degree is ideal for those looking to pursue a career in software development, data science, or computer engineering.

2. Master of Science in Artificial Intelligence: This degree path focuses on the development of artificial intelligence systems and their applications. It covers topics such as machine learning, natural language processing, computer vision, and robotics. This degree is ideal for those looking to pursue a career in research or development of AI systems.

3. Master of Science in Data Science: This degree path focuses on the development of data science tools and techniques. It covers topics such as data mining, machine learning, and data visualization. This degree is ideal for those looking to pursue a career in data analysis, data engineering, or data science.

Developing Trends:
1. Bachelor of Science in Computer Science: With the increasing demand for software developers, computer scientists, and data scientists, the demand for this degree path is also increasing. As technology advances, so does the need for computer scientists who can develop and maintain software systems.

2. Master of Science in Artificial Intelligence: With the increasing demand for AI systems, the demand for this degree path is also increasing. As AI technology advances, so does the need for AI experts who can develop and maintain AI systems.

3. Master of Science in Data Science: With the increasing demand for data scientists, the demand for this degree path is also increasing. As data science technology advances, so does the need for data scientists who can develop and maintain data science systems.