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AI Driven CI/CD Pipeline using GitLab

Build a strong foundation in enterprise CI/CD using GitLab, from pipeline design to secure deployments and merge-request quality gates. Learn how to optimize builds using caching and artifacts, apply governance controls, and implement DevSecOps scanning with AI-assisted pipeline creation and troubleshooting.

Duration:

Level:

Intermediate

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1500+ users onboarded

Who will Benefit from this Training?

DevOps Engineers
Platform Engineers
SRE Teams
Cloud Engineers
Developers maintaining pipelines
Security teams enforcing CI/CD governance

Training Objectives

Understand GitLab CI/CD fundamentals and enterprise pipeline requirements.
Build CI pipelines using .gitlab-ci.yml for real applications including build, unit tests, lint checks, and artifact creation.
Build secure CD pipelines with environment-based deployments, manual approvals for production, and controlled release workflows.
Use AI to accelerate pipeline creation: generate templates, create reusable CI templates, and auto-suggest improvements.
Use AI for troubleshooting: interpret pipeline logs, debug YAML errors, and fix rules and runner misconfigurations.
Integrate DevSecOps gates: secret scanning patterns, dependency scanning, container scanning, and optional IaC scanning.
Optimize pipelines using caching strategies, artifacts and dependencies, and parallel execution.
Implement enterprise governance: protected branches, protected environments, approvals, and auditability.
Deliver a complete AI-assisted GitLab CI/CD workflow as a capstone.

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Key training modules

Comprehensive, hands-on modules designed to take you from basics to advanced concepts

Module 1: GitLab CI/CD Fundamentals (Enterprise Requirements)
1. What GitLab CI/CD solves in modern delivery
2. Enterprise requirements (reliability, speed, compliance, traceability)
3. Pipeline concepts (stages, jobs, runners, artifacts)
4. GitLab variables, environments, and permissions overview
5. Hands-on: Activity: Map a commit-to-production workflow using GitLab stages and environments
Module 2: Building CI Pipelines with .gitlab-ci.yml (Build, Test, Lint, Artifacts)
1. .gitlab-ci.yml structure (stages, jobs, scripts)
2. Build and unit testing automation
3. Lint checks and quality gates
4. Artifacts creation and passing outputs between stages
5. Hands-on: Lab: Create a CI pipeline that builds, tests, lints, and publishes artifacts
Module 3: Secure CD with Environments and Controlled Releases
1. Environment-based deployments (dev, staging, production)
2. Manual approvals for production deployments
3. Release workflows (tag-based releases, release notes concept)
4. Rollback strategy basics (re-deploy last stable)
5. Hands-on: Lab: Build a CD pipeline with environment deployments and manual approval for production
Module 4: AI-Assisted Pipeline Creation (Templates and Reusable CI Components)
1. Using AI to generate .gitlab-ci.yml quickly
2. Creating reusable CI templates (include, extends, YAML anchors concepts)
3. Standardizing pipelines across projects using templates
4. AI-driven suggestions for best practices and improvements
5. Hands-on: Lab: Generate a pipeline with AI and refactor it into reusable templates
Module 5: AI-Assisted Troubleshooting (Logs, YAML Errors, Rules, Runners)
1. Reading GitLab pipeline logs and common failure patterns
2. Debugging YAML syntax, rules, and workflow conditions
3. Runner troubleshooting (tags, executors, permissions, network access)
4. Using AI to propose fixes and validate corrections
5. Hands-on: Lab: Break a pipeline intentionally and use AI to troubleshoot runner/rules/YAML issues
Module 6: DevSecOps Gates in GitLab CI/CD (Scan and Enforce)
1. Secret scanning patterns (preventing leaks, blocking merges)
2. Dependency scanning concepts (SCA) and severity thresholds
3. Container scanning in pipelines (image scanning workflow)
4. Optional IaC scanning concepts (Terraform checks)
5. Hands-on: Lab: Integrate security scans and enforce policy thresholds in CI
Module 7: Pipeline Optimization (Caching, Artifacts, Parallel Execution)
1. Caching strategies (dependencies, build cache)
2. Artifacts and dependencies (passing outputs safely between jobs)
3. Parallel execution patterns (split tests, matrix-like strategies)
4. Reducing pipeline time without reducing confidence
5. Hands-on: Lab: Add caching + parallel jobs and compare pipeline duration improvements
Module 8: Enterprise Governance (Protected Branches, Environments, Approvals, Auditability)
1. Protected branches and merge request approvals
2. Protected environments and deployment approvals
3. Audit trails (who changed pipeline, who deployed, what version)
4. Least privilege permissions for variables and runners
5. Hands-on: Workshop: Design an enterprise governance checklist for GitLab pipelines
Module 9: Capstone Project (AI-Assisted GitLab CI/CD Workflow)
1. Capstone goal: Deliver a production-style GitLab CI/CD pipeline with AI assistance
2. CI: build, unit tests, lint, artifacts
3. CD: environment-based deploys, manual production approval, controlled release
4. DevSecOps: secret scanning patterns, dependency scanning, container scanning, optional IaC scanning
5. Hands-on: Capstone Lab: Build and present a complete AI-assisted GitLab CI/CD workflow with evidence

Hands-on Experience with Tools

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Training Delivery Format

Flexible, comprehensive training designed to fit your schedule and learning preferences

Opt-in Certifications

AWS, Scrum.org, DASA & more

100% Live

on-site/online training

Hands-on

Labs and capstone projects

Lifetime Access

to training material and sessions

How Does Personalised Training Work?

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Skill-Gap Assessment

Analysing skill gap and assessing business requirements to craft a unique program

1

Personalisation

Customising curriculum and projects to prepare your team for challenges within your industry

2

Implementation

Supplementing training with consulting support to ensure implementation in real projects

3

Faster pipeline development: AI accelerates .gitlab-ci.yml authoring and pipeline design without starting from scratch.
Reduced pipeline failures: AI-assisted troubleshooting improves diagnosis of YAML, rules, runner, and build errors.
Secure delivery workflows by default: Security scans and guarded deployments become standard gates in every pipeline.
Standardized pipelines across teams: Reusable templates and include patterns enforce consistent practices and compliance.
Improved developer experience: Faster CI feedback with caching and better pipeline structure reduces friction.

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Frequently Asked Questions

1. What are the pre-requisites for this training?

The training does not require you to have prior skills or experience. The curriculum covers basics and progresses towards advanced topics.

2. Will my team get any practical experience with this training?

With our focus on experiential learning, we have made the training as hands-on as possible with assignments, quizzes and capstone projects, and a lab where trainees will learn by doing tasks live.

3. What is your mode of delivery - online or on-site?

We conduct both online and on-site training sessions. You can choose any according to the convenience of your team.

4. Will trainees get certified?

Yes, all trainees will get certificates issued by Uptut under the guidance of industry experts.