¶ GitLab CI CD (GitLab)
¶ Cloud Technologies
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GitLab CI/CD has become one of the most influential tools in the world of modern software development, even though its presence often feels subtle, woven quietly into the daily routines of developers, DevOps engineers, and cloud teams. What makes it so powerful is not the size of its interface or the complexity of its controls, but the philosophy it embodies—automation as a foundation, collaboration as a norm, and continuous improvement as a way of life. If cloud technology has transformed how we deploy and scale systems, then GitLab CI/CD has transformed how we build, test, release, and refine the software that lives within those systems.
This course invites you to dive into that transformative world. It helps you understand GitLab CI/CD not as a mere feature inside GitLab, but as a complete ecosystem that can shape how teams design workflows, automate tedious tasks, maintain code quality, and accelerate development cycles. In a world where speed and reliability define competitive advantage, CI/CD pipelines become more than tools—they become the nervous system of modern engineering.
At its heart, GitLab CI/CD offers a simple promise: automate everything that can be automated. Whether it’s testing code, building artifacts, running security checks, deploying to cloud environments, or rolling back problematic releases, GitLab brings all of this into a single, unified workflow. Instead of relying on a patchwork of external tools and custom scripts, teams can use GitLab’s built-in pipelines to streamline their entire lifecycle.
Automation is not just a convenience; it’s a necessity. In cloud-native development, infrastructure scales horizontally, environments shift constantly, and systems evolve rapidly. Manual processes simply cannot keep up. A deployment that takes hours of human effort becomes a bottleneck. A test suite that requires manual execution increases risk. A codebase that relies on personal discipline instead of automated checks becomes inconsistent. GitLab CI/CD eliminates these weaknesses by ensuring that every change is tested, validated, and ready for deployment.
But the magic of GitLab CI/CD lies not just in automation—it lies in its seamless integration with version control. Because the pipelines are defined inside the same repository where the code lives, they evolve naturally as the project evolves. Developers don’t need to jump between tools or manage additional configurations. They simply push their code, and GitLab does the rest. This fusion of version control and automation builds a culture of continuous integration––the practice of merging small, frequent changes rather than large, disruptive ones.
One of the most inspiring aspects of GitLab CI/CD is how it democratizes DevOps. Traditionally, building automated pipelines required deep knowledge of scripting languages, containerization, servers, and deployment workflows. GitLab lowers those barriers. Its YAML-based configuration is readable and approachable. Its runners handle much of the underlying complexity. Its templates provide ready-made pipelines for common languages and frameworks. As a result, even small teams and individual developers can embrace CI/CD without feeling overwhelmed.
Throughout this course, you’ll explore how GitLab CI/CD functions as an anchor point for countless cloud workflows. You’ll learn how it integrates with Kubernetes, AWS, Google Cloud, Azure, and on-premises infrastructure. You’ll see how developers use it to build Docker images automatically, push them to registries, run cloud-native tests, and trigger deployments in clusters. This tight integration with cloud environments turns GitLab CI/CD into a cornerstone of continuous delivery and continuous deployment.
You will also discover how GitLab supports security at every stage of development. Security is no longer something that teams add at the end—it has become part of the development cycle itself. GitLab CI/CD makes security scanning a natural component of the pipeline. It checks dependencies for vulnerabilities, scans container images, analyzes code for potential flaws, and flags risky configurations. This shift-left approach empowers teams to detect and resolve problems before they reach production.
One of the most exciting aspects of GitLab CI/CD is the transparency it brings to engineering workflows. In many organizations, deployments used to feel mysterious—happening behind closed doors, orchestrated by a handful of specialists. GitLab changes that. Every job, every stage, every artifact, every environment update becomes visible. Teams can track changes, audit deployments, compare pipeline runs, view logs, and troubleshoot issues collaboratively. This visibility builds trust, reduces confusion, and strengthens accountability.
As you explore GitLab CI/CD deeply, you will begin to notice patterns that define strong pipelines. Good CI/CD reflects good engineering discipline. Pipelines that run fast, fail fast, and give clear feedback help teams iterate without fear. Pipelines that enforce code quality ensure consistency. Pipelines that automate deployments reduce downtime and increase confidence. These best practices reflect broader truths about DevOps—clarity matters, speed matters, consistency matters, and automation brings them all together.
Another theme that emerges when working with GitLab CI/CD is flexibility. Every project has its own shape, and GitLab respects that. Whether you’re building a microservices architecture, a monolithic application, a machine learning pipeline, or a static site, GitLab adapts. The same platform can run linting jobs, compile binaries, train ML models, deploy containers, or run scheduled maintenance tasks. This versatility ensures that as your project grows or changes direction, your CI/CD system grows with it.
This course will also help you understand how GitLab CI/CD supports collaboration. Modern engineering is not about isolated contributions—it’s about coordinated efforts. Teams need consistent branches, protected environments, code review rules, merge strategies, and controlled release cycles. GitLab ties all these elements together. It connects issues to pipeline runs, merge requests to test results, environments to deployments, and monitoring tools to alerts. When everyone sees the same information, communication improves naturally.
A key insight you’ll gain over these 100 articles is that GitLab CI/CD is not only for large enterprises—it empowers individuals too. Solo developers can automate tests, enforce style rules, deploy projects without stress, and maintain professional-level workflows. For small teams, GitLab CI/CD becomes the backbone that prevents chaos as projects grow. For large organizations, it becomes the glue that connects dozens of teams, hundreds of repositories, and thousands of pipelines.
You’ll also explore the deeper engineering principles behind CI/CD:
• Why small changes reduce risk
• How automation improves quality
• Why testing early prevents costly bugs
• How deployment frequency correlates with stability
• Why reproducibility matters in cloud-native systems
• How observability strengthens resilience
These principles are not limited to GitLab—they’re part of the cultural shift that defines modern cloud technologies. GitLab simply makes them easier to adopt.
Another area of exploration will be GitLab’s advanced features:
• Dynamic environments for previewing changes
• Pipeline caching for faster builds
• Child pipelines for complex projects
• Multi-runner strategies for distributed workloads
• Secret management for secure automation
• Infrastructure-as-code pipelines for provisioning environments
• Blue-green and rolling deployments
• Monitoring and rollback mechanisms
These capabilities allow GitLab CI/CD to support everything from small apps to global-scale platforms.
As you progress through the course, you’ll come to see GitLab CI/CD as more than a tool. It becomes a mindset—one centered around continuous improvement, automation-first thinking, and confidence in every release. It teaches teams to iterate boldly, test thoroughly, deploy responsibly, and respond quickly. It empowers developers to think beyond code and consider the full lifecycle of their work.
By the end of this learning journey, GitLab CI/CD will feel intuitive, natural, and empowering. You will understand how pipelines reflect engineering culture. You will know how to design workflows that support real-world demands. You will be able to build environments where innovation happens safely and quickly. Most importantly, you’ll carry with you a deeper appreciation for how automation shapes the future of cloud technologies.
As you begin this first article, bring curiosity and openness. GitLab CI/CD is not just a feature—it is an invitation to build better systems, cleaner workflows, and more resilient applications. It is a tool that elevates developers, accelerates teams, and transforms ideas into reliable cloud-native solutions.
¶ GitLab CI-CD (GitLab)
¶ Beginner Level (Getting Started with GitLab CI/CD)
1. Introduction to Continuous Integration and Continuous Delivery
2. What is GitLab CI/CD and Why Use It for Cloud Development?
3. Setting Up Your GitLab Account and Project
4. Navigating the GitLab Interface for CI/CD
5. Understanding GitLab CI/CD Pipeline Basics
6. Creating Your First GitLab CI/CD Pipeline
7. Understanding GitLab Runner and How It Works
8. GitLab CI/CD Workflow: Push, Build, Test, Deploy
9. Basic GitLab CI/CD Configuration with .gitlab-ci.yml
10. Running Your First Build with GitLab CI/CD
11. How to Configure Jobs in GitLab CI/CD
12. Introduction to GitLab Runners and Executor Types
13. How to Define Stages in GitLab CI/CD Pipelines
14. Understanding GitLab’s Automatic Pipeline Triggering
15. Building Your First CI/CD Job in GitLab
16. Working with GitLab CI/CD Variables and Secrets
17. Managing Artifacts and Caching in GitLab CI/CD
18. Testing Code with GitLab CI/CD Pipelines
19. Introduction to GitLab CI/CD Runners and Docker Executors
20. Setting Up Notifications for Pipeline Status in GitLab
¶ Intermediate Level (Expanding Your GitLab CI/CD Skills)
21. Advanced Configuration of .gitlab-ci.yml File
22. Using GitLab CI/CD for Automated Testing
23. GitLab and Cloud Integration: Deploying to AWS, GCP, and Azure
24. Parallel and Sequential Job Execution in GitLab CI/CD
25. Using GitLab CI/CD for Multi-Branch Pipelines
26. Conditional Execution of Jobs in GitLab Pipelines
27. GitLab CI/CD for Deploying Containerized Applications
28. Setting Up Continuous Integration for Web Applications with GitLab
29. Using GitLab CI/CD with Kubernetes for Cloud-Native Deployments
30. GitLab CI/CD for Microservices Architecture
31. Building and Deploying Docker Containers with GitLab CI/CD
32. Using GitLab CI/CD with Serverless Frameworks
33. Managing Secrets and Environment Variables in GitLab CI/CD
34. Creating and Managing Custom GitLab Runners
35. Building a Multi-Environment Deployment Pipeline in GitLab CI/CD
36. Optimizing GitLab CI/CD Pipelines for Speed and Efficiency
37. GitLab CI/CD for Database Migrations and Management
38. Creating Reusable Templates for GitLab CI/CD Pipelines
39. GitLab CI/CD and Artifact Management: Storing and Using Artifacts
40. Integrating GitLab CI/CD with External Tools (Slack, Jira, etc.)
¶ Advanced Level (Mastering GitLab CI/CD and Cloud DevOps)
41. GitLab CI/CD for Multi-Cloud Deployments
42. Using GitLab CI/CD with Infrastructure as Code (Terraform)
43. Automating Cloud Resource Provisioning with GitLab CI/CD
44. Advanced GitLab CI/CD Pipeline Strategies for Microservices
45. Building and Deploying Serverless Applications with GitLab CI/CD
46. Optimizing Large-Scale GitLab CI/CD Pipelines
47. GitLab CI/CD with Blue/Green and Canary Deployments
48. Continuous Security: Implementing SAST/DAST with GitLab CI/CD
49. Integrating GitLab CI/CD with Cloud Monitoring and Logging
50. Advanced Secrets Management with GitLab CI/CD and HashiCorp Vault
51. Automating Multi-Region Deployments with GitLab CI/CD
52. GitLab CI/CD for Edge Computing and IoT Applications
53. Advanced Docker Builds and Deployment Strategies with GitLab CI/CD
54. Implementing GitLab CI/CD for Hybrid Cloud Applications
55. Using GitLab CI/CD with Cloud Databases (e.g., RDS, Cloud SQL)
56. Automating Application Monitoring and Alerts with GitLab CI/CD
57. Integrating GitLab CI/CD with Kubernetes for Cloud-Native Deployments
58. GitLab CI/CD for Secure Cloud-based Deployments
59. Using GitLab CI/CD for Continuous Delivery in DevOps
60. Advanced GitLab Pipeline Scripting with Shell and Docker
61. Implementing Continuous Compliance with GitLab CI/CD
62. Running CI/CD Jobs on Auto-Scaling Cloud Infrastructure with GitLab
63. Managing Cloud Infrastructure Lifecycle with GitLab CI/CD
64. GitLab CI/CD for Cost Optimization in Cloud Environments
65. Advanced Integration of GitLab CI/CD with AWS CloudFormation
66. Scaling GitLab CI/CD Pipelines for Large Teams and Projects
67. GitLab CI/CD for Cloud-Native Application Management
68. Optimizing Cloud Storage Management with GitLab CI/CD
69. Automating Cloud Service Updates and Patches with GitLab CI/CD
70. Multi-Tenant Cloud Deployments with GitLab CI/CD
71. GitLab CI/CD for DevSecOps: Integrating Security Into CI/CD
72. Automating Cloud-Based Data Pipelines with GitLab CI/CD
73. Managing GitLab CI/CD in Multi-Region Cloud Environments
74. Using GitLab CI/CD for Automated Cloud Network Configuration
75. Integrating GitLab CI/CD with GitOps for Continuous Delivery
76. Managing Cloud Costs with GitLab CI/CD Automation
77. GitLab CI/CD for Real-Time Cloud Application Scaling
78. Using GitLab CI/CD for Cloud-Based Big Data Processing
79. Setting Up Immutable Infrastructure with GitLab CI/CD and Kubernetes
80. Automating Serverless Deployments in Cloud with GitLab CI/CD
81. GitLab CI/CD for Continuous Delivery in Multi-Cloud Environments
82. Advanced GitLab CI/CD Pipelines with Parallel and Conditional Stages
83. Building and Managing Multi-Cloud CI/CD Pipelines with GitLab
84. GitLab CI/CD and Cloud Security: Automating Vulnerability Scanning
85. Automating Cloud Backup and Restore with GitLab CI/CD
86. Managing Cloud Infrastructure as Code with GitLab CI/CD and Terraform
87. GitLab CI/CD for Large-Scale, High-Availability Cloud Applications
88. GitLab CI/CD for High-Performance Computing in the Cloud
89. Building Event-Driven Pipelines in GitLab CI/CD for Cloud Applications
90. GitLab CI/CD for Cloud DevOps Automation: Advanced Techniques
91. Building Hybrid Cloud CI/CD Pipelines with GitLab
92. GitLab CI/CD with Cloud Cost Management and Optimization Strategies
93. Running AI and Machine Learning Workflows in Cloud with GitLab CI/CD
94. Implementing DevOps Best Practices with GitLab CI/CD in the Cloud
95. Using GitLab CI/CD to Automate Disaster Recovery for Cloud Services
96. Implementing A/B Testing and Feature Flags in GitLab CI/CD Pipelines
97. Advanced GitLab CI/CD: Custom Runners, Executors, and Cloud Infrastructure
98. GitLab CI/CD for Cloud-Native Security Operations
99. GitLab CI/CD and Cloud AI: Automating ML Pipelines and Models
100. The Future of GitLab CI/CD: Innovations in Cloud DevOps and Automation