+44 7459 302492
support@uplatz.com
4.7 (2 reviews)
( 10 Students )
( 10 Students )
GitLab CI/CD
Master GitLab CI/CD from scratch and automate your software development pipelines for efficient, reliable, and scalable DevOps workflows
Course Duration: 10 Hours
Price Match Guarantee
Full Lifetime Access
Access on any Device
Technical Support
Secure Checkout
Course Completion Certificate
91% Started a new career
BUY THIS COURSE (USD 12 USD 41 )-
84% Got a pay increase and promotion
New & Hot
Trending
Great Value
Coming Soon
Students also bought -
-
- DevOps
- 20 Hours
- USD 12
- 1677 Learners
-
- Career Path - DevOps Engineer
- 100 Hours
- USD 45
- 372 Learners
-
- Platform Engineering for Cloud Architects & DevOps Engineers
- 41 Hours
- USD 12
- 640 Learners
GitLab CI/CD - Automating DevOps Pipelines for Continuous Integration and Delivery Online Course
GitLab CI/CD - Automating DevOps Pipelines for Continuous Integration and Delivery is an all-in-one, practical, and hands-on course designed to help developers, DevOps engineers, and system administrators master one of the most powerful automation tools in the DevOps ecosystem—GitLab CI/CD. This course offers a structured path from the fundamentals of GitLab to building robust, efficient, and secure continuous integration and continuous delivery pipelines.
GitLab CI/CD is a built-in part of the GitLab DevOps platform that provides automation capabilities for testing, building, and deploying applications directly from the source repository. Unlike external CI/CD tools that require integrations and plugins, GitLab offers a unified experience where source control, pipelines, issues, containers, and monitoring are integrated within a single interface.
What sets GitLab CI/CD apart is its simplicity in configuration, its YAML-based .gitlab-ci.yml files, and its powerful support for automation jobs in a wide variety of environments—including Docker, Kubernetes, cloud providers, and on-premise systems. GitLab’s ability to manage the entire DevOps lifecycle makes it unique among competitors like Jenkins, CircleCI, and Travis CI.
This course begins by laying the groundwork—explaining what CI/CD is, the benefits of pipeline automation, and how GitLab structures jobs, stages, runners, and artifacts. You'll learn how to write .gitlab-ci.yml files from scratch, define jobs, manage conditional workflows, and create reusable templates with includes and anchors.
As you progress, the course dives deep into building pipelines for various use cases—Node.js applications, Python microservices, Java builds, and container-based deployments using Docker and Kubernetes. You'll also explore security scanning, testing best practices, and advanced GitLab features like Auto DevOps and GitLab Pages.
The course is rich in practical exercises. You'll build real-world pipelines that:
- Automatically test and lint code on every commit
- Build Docker containers and push to GitLab Container Registry
- Deploy applications to staging and production environments
- Scan code for vulnerabilities using GitLab SAST and Dependency Scanning
- Send notifications through Slack and email upon pipeline completion
By the end of the course, you will have the knowledge and skills to build enterprise-grade CI/CD pipelines using GitLab, automate your deployments, and streamline the software delivery process with minimal human intervention.
Whether you're a developer looking to improve your workflow, a DevOps engineer optimizing build pipelines, or a team lead enforcing best practices, this course provides a comprehensive, career-focused foundation for mastering GitLab CI/CD.
Course Objectives Back to Top
By the end of this course, you will be able to:
- Understand the concepts of Continuous Integration and Continuous Delivery.
- Set up GitLab runners and configure .gitlab-ci.yml files from scratch.
- Build multi-stage CI/CD pipelines with custom jobs and triggers.
- Automate testing, build, and deployment workflows using GitLab CI/CD.
- Deploy containerized applications to Kubernetes using GitLab.
- Implement security scanning and code quality gates in your pipelines.
- Use caching, artifacts, and environments effectively.
- Monitor pipelines and integrate GitLab CI/CD with Slack, Jira, and Docker Hub.
Course Syllabus Back to Top
GitLab CI/CD Course Syllabus
Module 1: Introduction to GitLab CI/CD
- What is CI/CD?
- GitLab DevOps Overview
- GitLab Architecture and Features
Module 2: Setting Up GitLab and Runners
- Installing GitLab Locally and on Cloud
- Registering and Managing GitLab Runners
- Shared vs Specific Runners
Module 3: GitLab CI/CD Pipeline Basics
- Anatomy of .gitlab-ci.yml
- Defining Jobs and Stages
- Pipeline Visualization
Module 4: Working with Artifacts and Caches
- Using Artifacts for File Sharing
- Managing Cache for Speed Optimization
Module 5: Advanced Pipeline Features
- Conditional Jobs and Rules
- Parallel and Manual Jobs
- Triggers and Schedules
Module 6: Building CI/CD for Applications
- Node.js Project Pipeline
- Python Flask Microservice Pipeline
- Java Maven Pipeline
- Docker Container Pipeline
Module 7: GitLab Registry and Docker Deployment
- Docker Builds and Push to Registry
- Deploying Containers with GitLab CI/CD
- Environment Variables and Secrets
Module 8: Kubernetes Integration
- Deploying to Kubernetes with Helm
- GitLab Auto DevOps
- Rollback and Deployment Strategies
Module 9: Security and Testing
- SAST and DAST in Pipelines
- Dependency Scanning
- Container Scanning
Module 10: Notifications and Integration
- Slack and Email Alerts
- Jira Integration
- Webhooks
Module 11: GitLab Pages and Documentation
- Static Site Deployment
- API Documentation Hosting
Module 12: Real-World Projects and Templates
- End-to-End CI/CD Project
- Reusable Pipeline Templates
- GitOps in Practice
Module 13: GitLab CI/CD Interview Questions & Answers
- Key Concepts, Practical Scenarios, and Best Practices
Certification Back to Top
Upon successful completion of this course, learners will receive a Certificate of Completion from Uplatz verifying their proficiency in GitLab CI/CD. This certification is a powerful addition to your resume or LinkedIn profile and highlights your ability to automate software workflows, ensure code quality, and accelerate release cycles. It proves your expertise in DevOps automation and your readiness to contribute to or lead CI/CD practices in any organization.
Career & Jobs Back to Top
GitLab CI/CD skills are in high demand as organizations increasingly adopt DevOps and continuous delivery pipelines. Professionals with GitLab CI/CD expertise can explore roles such as:
- DevOps Engineer
- CI/CD Pipeline Engineer
- Automation Engineer
- Build & Release Engineer
- Cloud Engineer
- Software Developer (DevOps focused)
Industries ranging from fintech to healthcare, e-commerce to enterprise IT, are actively recruiting individuals who can manage efficient, secure, and automated CI/CD workflows. Freelancers and consultants with GitLab expertise can also build lucrative careers offering DevOps automation solutions.
Interview Questions Back to Top
1. What is GitLab CI/CD?
GitLab CI/CD is a built-in tool in GitLab that automates the process of software integration, testing, and deployment using pipelines defined in .gitlab-ci.yml.
GitLab CI/CD is a built-in tool in GitLab that automates the process of software integration, testing, and deployment using pipelines defined in .gitlab-ci.yml.
2. How does a GitLab CI/CD pipeline work?
A pipeline consists of stages (like build, test, deploy) and jobs within each stage. When code is pushed, GitLab executes the jobs based on the configuration defined in the YAML file.
A pipeline consists of stages (like build, test, deploy) and jobs within each stage. When code is pushed, GitLab executes the jobs based on the configuration defined in the YAML file.
3. What is a GitLab Runner?
A GitLab Runner is an agent that executes the jobs defined in the pipeline. Runners can be shared, specific, or group runners, and can run in Docker, Kubernetes, or directly on VMs.
A GitLab Runner is an agent that executes the jobs defined in the pipeline. Runners can be shared, specific, or group runners, and can run in Docker, Kubernetes, or directly on VMs.
4. What is the purpose of artifacts and cache in GitLab CI?
Artifacts are used to store job results (e.g., build files), while cache speeds up pipelines by storing reusable data like dependency packages.
Artifacts are used to store job results (e.g., build files), while cache speeds up pipelines by storing reusable data like dependency packages.
5. How do you trigger a pipeline manually?
Pipelines can be triggered manually using the GitLab UI with a "Run Pipeline" button or through an API call with custom variables.
Pipelines can be triggered manually using the GitLab UI with a "Run Pipeline" button or through an API call with custom variables.
6. What is the difference between only, except, and rules?
only and except control job execution based on branches or tags. rules provide more granular conditions and are preferred in modern configurations.
only and except control job execution based on branches or tags. rules provide more granular conditions and are preferred in modern configurations.
7. How can you secure sensitive data in GitLab CI/CD?
Use GitLab CI/CD variables, secret vaults, or HashiCorp Vault integrations to store credentials securely and inject them into jobs.
Use GitLab CI/CD variables, secret vaults, or HashiCorp Vault integrations to store credentials securely and inject them into jobs.
8. What is GitLab Auto DevOps?
Auto DevOps provides pre-configured CI/CD templates for building, testing, and deploying applications automatically using best practices.
Auto DevOps provides pre-configured CI/CD templates for building, testing, and deploying applications automatically using best practices.
9. Can you use GitLab CI/CD with external Git repositories?
Yes, GitLab CI/CD can be configured to work with external Git repositories via mirror setups or third-party integrations.
Yes, GitLab CI/CD can be configured to work with external Git repositories via mirror setups or third-party integrations.
10. How do you handle deployment rollbacks?
Deployments can be rolled back by defining rollback jobs or using GitLab environments and versioned container images for safe re-deployment.
Deployments can be rolled back by defining rollback jobs or using GitLab environments and versioned container images for safe re-deployment.
Course Quiz Back to Top
FAQs
Back to Top