10 GitHub Repositories to Master Cloud Computing

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Cloud computing is no longer limited to just VPS (Virtual Private Servers) or storage providers — it has evolved into so much more. Today, we use cloud computing for automation, website deployments, application development, machine learning, data engineering, integrating managed services, and countless other use cases.

Learning cloud computing can give you a significant edge in a variety of fields, including data science, as employers often prefer individuals with hands-on experience in dealing with cloud infrastructure. 

In this article, we will explore 10 GitHub repositories that can help you master the core concepts of cloud computing. These repositories offer courses, content, projects, examples, tools, guides, and workshops to provide a comprehensive learning experience.

1. freeCodeCamp/freeCodeCamp

 
freeCodeCamp is the most popular GitHub repository, and it comes with tons of content and tutorials on various topics related to cloud computing, DevOps, and software engineering. 

Why It’s Useful:

• Beginner-friendly.
• Offers hands-on projects, interactive lessons, and coding challenges to strengthen your fundamentals.
• Covers topics like APIs and microservices, which are important in cloud development.

2. learntocloud/learn-to-cloud

 
This repository is a goldmine for those starting with cloud computing. It provides a structured curriculum to help you learn the fundamentals of cloud technologies and platforms like AWS, Azure, and Google Cloud. You will learn Linux and Bash, programming, cloud platform fundamentals, DevOps fundamentals, and cloud security. 

Why It’s Useful:

• Beginner-focused, with a step-by-step approach.
• Covers key cloud concepts like DevOps.
• Includes practical guidance on certifications and resume-building for cloud-related jobs.

3. aws/amazon-sagemaker-examples

 
If you are interested in learning how to train, evaluate, and deploy machine learning models on the Cloud, this is the perfect guide for you. It features Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using Amazon SageMaker.

Why It’s Useful:

• Focuses on machine learning in the cloud, a growing field.
• Offers real-world examples, such as fraud detection and sentiment analysis.
• Provides insights into AWS SageMaker, a key AWS service for data scientists and machine learning engineers.

4. Azure/azure-quickstart-templates

 
Azure Quickstart Templates is a collection of over 1,000 templates to help you deploy various Azure services using Azure Resource Manager (ARM).

Why It’s Useful:

• Great for learning how to use infrastructure-as-code (IaC) on Azure.
• Covers a wide range of scenarios, including web apps, databases, and Kubernetes clusters.
• Helps you understand how to automate deployments and manage cloud resources effectively.

5. GoogleCloudPlatform/data-science-on-gcp

 
This repository contains code and resources of the book Data Science on the Google Cloud Platform. It’s an excellent resource for learning how to use Google Cloud for data science and machine learning projects.

Why It’s Useful:

• Ideal for data scientists and engineers looking to work on Google Cloud Platform (GCP).
• Focuses on real-world use cases, such as data pipelines and machine learning workflows.
• Helps you understand GCP services like BigQuery, Dataflow, and AI Platform.

6. joseadanof/awesome-cloudnative-trainings

 
A fantastic collection of free cloud-native training courses, this repository focuses on technologies like GitOps, Kubernetes, Prometheus, and other Cloud Native Computing Foundation (CNCF) projects.

Why It’s Useful:

• Offers free, high-quality training materials.
• Covers popular cloud-native tools and concepts like containerization and microservices.
• Helps you prepare for certifications like Kubernetes Certified Administrator (CKA).

7. dgkanatsios/CKAD-exercises

If you are preparing for the Certified Kubernetes Application Developer (CKAD) exam, this repository is a must. It features a collection of exercises specifically designed to help you practice Kubernetes concepts.

Why It’s Useful:

• Perfect for Kubernetes beginners and advanced users looking to get certified.
• Includes hands-on exercises to practice key Kubernetes workflows like pod management, networking, and deployments.
• Provides explanations and solutions for each exercise.

8. contino/terraform-learn

 
Terraform is one of the most popular tools for infrastructure-as-code (IaC). This repository provides a baseline for using Terraform to deploy compute and networking infrastructure on AWS, Azure, and Google Cloud.

Why It’s Useful:

• Excellent for learning multi-cloud IaC practices.
• Covers best practices for writing Terraform scripts.
• Helps you understand how to deploy scalable cloud infrastructure.

9. NotHarshhaa/into-the-devops

 
This repository is a comprehensive guide to DevOps and cloud computing. It covers a wide range of topics, including Linux, Docker, Kubernetes, Terraform, AWS, Azure, GCP, and more.

Why It’s Useful:

• All-in-one resource for cloud computing and DevOps.
• Includes interview questions, making it ideal for job preparation.
• Covers both theoretical concepts and practical tools.

10. bregman-arie/devops-exercises

 
This GitHub repository is a fantastic resource for anyone looking to deepen their understanding of DevOps and SRE concepts. With over 2,600 questions and exercises, it covers a wide range of technical topics, from Linux and Kubernetes to cloud platforms like AWS and GCP, as well as CI/CD, databases, and more.

Why It’s Useful:

• Provides a broad set of exercises to help you explore and practice key DevOps concepts, making it ideal for self-learning or interview preparation.
• Covers both foundational and advanced topics, offering value to beginners and experienced professionals alike.
• Continuously updated and open for contributions, allowing the community to expand and refine the content over time.

Conclusion

 
Mastering cloud computing requires a mix of theoretical knowledge and hands-on experience. In this article, we have learned about various resources, from tutorials and exercises to infrastructure-as-code templates and machine-learning examples. Whether you're just starting or looking to specialize in a specific area like Kubernetes or cloud-native development, these repositories will guide you on your learning journey.
 
 

Abid Ali Awan (@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master's degree in technology management and a bachelor's degree in telecommunication engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.