Mastering DevOps with Generative AI

1. Traditional DevOps Path
   1. Git architecture, workflow (commit, branch, merge, rebase)
   2. Branching strategies: GitFlow, GitHub Flow, trunk-based
   3. Pull requests, code review, merge conflict resolution
   4. Git hooks, CI triggers from commits
   5. GitHub basics: repos, forks, branches, issues, PRs
2. GenAI-enhanced path
   1. Prompt-based generation of Git workflows, commit messages, PR templates
   2. Using Copilot / GPT to auto-generate commit message summaries or PR descriptions
   3. AI-powered code review comments via GitHub Copilot or Claude
   4. Automating conflict resolution suggestions using LLMs
3. Hands-On Labs
   1. Lab 1: Perform branching, committing, merging, and PR creation manually on a sample project.
   2. Lab 2: Use Copilot / GPT to generate commit messages and PR descriptions for a feature branch
   3. Lab 3: Raise a PR with intentional anti-patterns → use Claude or Copilot Chat to generate review comments.
   4. Lab 4: Simulate a merge conflict → ask an LLM to suggest a resolution

1. Traditional path
   1. Setting up Jenkins / GitHub Actions / GitLab CI for builds
   2. Installing dependencies, running unit tests, static analysis
   3. Artifact storage, versioning, packaging
   4. Managing build environments, caching, parallelism
2. GenAI-enhanced path
   1. Natural language → CI pipeline generation (Jenkinsfile)
   2. AI for test scaffolding, automatic test-case generation
   3. AI-driven build optimization
   4. Integrating AI linting / security checks automatically
      
3. Hands-On Labs
   1. Set up Jenkins or GitHub Actions for a sample project.
   2. Build and test code in the pipeline.
   3. Generate the same pipeline using GenAI and compare results.
   4. Analyze and optimize pipelines with GenAI suggestions.

1. Traditional Path
   1. Introduction to Ansible
      
   2. Writing basic playbooks, roles, and inventory files manually
      
   3. Idempotency, handlers, variables
      
   4. Using modules, templating (Jinja2), loops and conditionals
      
   5. Configuration drift detection and remediation
      
   6. Manual documentation of playbooks
2. GenAI-Enhanced Path
   1. Ansible Lightspeed for AI-assisted playbook authoring:
      
      1. Suggests code completions inside VS Code
         
      2. Converts natural language → Ansible YAML tasks
         
      3. Learns from internal playbooks for context-aware suggestions
   2. Generating full playbooks and roles from plain language prompts using Claude/GPT
   3. Converting existing Bash or shell scripts into structured Ansible tasks via LLMs
   4. Refactoring large playbooks into modular roles using Claude/Qwen (long-context models)
   5. Auto-generating documentation for playbooks and roles
3. Hands-On Labs
   1. Write a basic Ansible playbook and inventory manually.
   2. Use Ansible Lightspeed to generate the same configuration.
   3. Convert a sample shell script into an Ansible playbook with GPT.
   4. Refactor a large playbook using Claude/Qwen.
   5. Auto-document a role using GenAI.

1. Traditional path:
   1. Writing Dockerfiles manually (FROM, RUN, COPY, CMD)
   2. Multi-stage builds, caching, optimizations
   3. Building images, pushing to registry, versioning
   4. Best practices (small base images, layers, security)
2. GenAI-enhanced path:
   1. 1. Prompt-based Dockerfile generation for multiple runtimes
      2. AI suggestions to optimize image size
      3. Auto-commenting Dockerfiles for readability
3. Hands-On Labs
   1. Write a basic Dockerfile manually for a sample app.
   2. Build, tag, and push images to Docker Hub.
   3. Prompt GenAI to generate an optimized Dockerfile for the same app.
   4. Compare manual vs AI-generated versions.
   5. Apply GenAI-suggested optimizations to improve image size and security.

1. Traditional Path :
   1. Kubernetes resource types: Deployment, Service, ConfigMap, Secret, Ingress
   2. Rolling updates, readiness & liveness probes, horizontal auto-scaling
   3. Namespaces, Network Policies, RBAC
   4. Helm
   5. Manual troubleshooting using kubectl, logs, and events
2. GenAI-Enhanced Path
   1. Natural language → K8s YAML manifests (Deployments, Services, HPAs)
   2. Imperative → declarative conversion: transform kubectl commands into YAML using LLMs
   3. AI-generated Helm charts & overlays from high-level architecture descriptions
   4. AI-driven suggestions for resource sizing, probes, security policies, RBAC rules
   5. K8sGPT for AI-assisted cluster troubleshooting & RCA:
      1. Analyze pod failures, CrashLoops, misconfigurations
      2. Suggest fixes (e.g., missing ConfigMaps, resource limits, incorrect selectors)
      3. Integrate into kubectl workflows or as a controller
3. Hands-On Labs
   1. Lab 1: Manually write a Kubernetes Deployment & Service manifest → then generate the same using GenAI tools.
   2. Lab 2: Convert imperative kubectl run/expose commands to declarative YAML using LLMs.
   3. Lab 3: Prompt an LLM to generate Helm overlays for a multi-environment setup.
   4. Lab 4: Deploy a broken manifest intentionally → use K8sGPT to diagnose and fix the issue interactively.
   5. Lab 5: Ask an LLM to add HPA, probes, and RBAC to an existing manifest and validate.

1. Traditional Path
   1. Writing Terraform resource blocks, variables, and outputs manually
   2. Creating reusable modules and referencing dependencies
   3. State management, remote backends, workspaces
   4. Handling syntax errors, interpolation, and provider issues
   5. terraform plan / apply lifecycle and manual debugging
2. GenAI-Enhanced Path
   1. 1. HashiCorp Terraform AI Assistant for:
         1. Inline suggestions in Terraform Cloud & VS Code
         2. Converting natural language into Terraform resource blocks
         3. Providing security and best-practice recommendations
         4. Auto-completing variables, outputs, and module structure
      2. Prompting external LLMs (GPT / Claude / Qwen) to:
         1. Generate complete Terraform modules from architecture descriptions
         2. Refactor existing Terraform codebases with long-context understanding
         3. Generate variables.tf, outputs.tf, backend configs automatically

1. Hands-On Labs (Trainer-Led)
   1. Lab 1: Write a simple Terraform configuration manually (e.g., create an S3 bucket or VM).
   2. Lab 2: Use Terraform AI Assistant to generate the same configuration from a natural-language prompt.
   3. Lab 3: Use an external LLM (Claude or GPT) to scaffold a multi-resource module (VPC + EC2 + S3).
   4. Lab 4: Refactor a messy Terraform repository using Claude/Qwen for modularization.
   5. Lab 5: Validate a configuration using Terraform AI Assistant and analyze its optimization suggestions.

1. Traditional path:
   1. Setting up Prometheus, Grafana, ELK / EFK stacks
      
   2. Collecting metrics, logs, alert rules
      
   3. Manual root cause analysis using dashboards & logs
      
   4. Incident runbooks, alert escalation
2. GenAI-enhanced path:
   1. Feed logs/metrics into LLM for automated RCA
      
   2. Use conversational agents (Slack bots) to triage alerts
      
   3. Generate incident reports and runbooks from incidents
      
   4. Predict anomalies and suggest threshold tuning
3. Hands-On Labs
   1. Lab 1: Set up a Prometheus + Grafana stack and visualize application metrics.
   2. Lab 2: Simulate an application incident and analyze manually using logs and metrics.
   3. Lab 3: Feed the same logs and alert messages into a GenAI model (e.g., GPT or Amazon Q) to perform automated RCA.
   4. Lab 4: Integrate a Slack chatbot that answers: “What failed and why?” using LLM insights.
   5. Lab 5: Generate an AI-written incident report from raw logs and alerts.

This module brings everything students have learned together — from version control to CI/CD, IaC, containerization, Kubernetes orchestration, monitoring, and finally GenAI augmentation across the entire pipeline.
It’s designed as a guided end-to-end project, integrating both the traditional DevOps workflow and the GenAI-powered approach.

The goal is not only to build a pipeline but to prove how GenAI accelerates, simplifies, and enhances each DevOps phase.

1. Step 1 – Version Control Setup
   1. Initialize Git repo on GitHub.
   2. Organize branches: main, develop, feature/*.
   3. Use Copilot CLI to auto-generate commit messages and PR templates.
2. Step 2 – Continuous Integration
   1. Configure Jenkins / GitHub Actions to:
      1. Install dependencies
      2. Run tests
      3. Perform code analysis with SonarQube
      4. Build artifact and push to Docker Hub
      5. Use GenAI to generate Jenkinsfile or workflow YAML from natural-language descriptions.
3. Step 3 – Infrastructure Provisioning
   1. Author Terraform scripts for VM/network setup.
   2. Provision infrastructure automatically.
   3. Use Terraform AI Assistant or Claude for resource block generation and validation.
4. Step 4 – Configuration Management
   1. Use Ansible to install dependencies and configure app servers.
   2. Generate playbooks using Ansible Lightspeed or GPT from plain-English prompts.
5. Step 5 – Containerization & Deployment
   1. Create Dockerfiles and K8s manifests for the app.
   2. Deploy to Kubernetes via Helm or direct YAMLs.
   3. Use GenAI to generate YAMLs, and K8sGPT for diagnostics if issues occur.
6. Step 6 – Monitoring & Incident Response
   1. Set up Prometheus + Grafana for metrics, ELK for logs.
   2. Simulate an outage.
   3. Feed alerts to a GenAI model (Amazon Q / GPT) for automated root-cause analysis.
   4. Generate a post-incident report automatically.