AI Integrated DevOps, AWS and Linux course
- Description
About DevOps and AI Integrated Syllabus
About DevOps and AI Integrated Course Features
- Comprehensive Curriculum
- Expert Instructors
- Interactive Learning
- Weekly Tests
- Feedback and Assessment
- Real time Projects
- Continuous Support
- ISO Certification
- Mock Interview
- Unlimited Placement Support
Ethical Hacking Course Syllabus
Introduction to DevOps & AI in DevOps
-DevOps principles, lifecycle & SDLC mapping
-Dev vs Ops challenges and AI-powered solutions (predictive monitoring,
automated RCA)
-DevOps culture (CALMS: Collaboration, Automation, Lean, Measurement,
Sharing)
-Introduction to CI/CD pipelines, Infrastructure as Code (IaC)
-AI in DevOps (AIOps): anomaly detection, log analysis, predictive scaling
Git & GitHub – Version Control with AI Assistance
-What are Version Control & Git fundamentals
-Git CLI: init, clone, add, commit, push, pull
-Branching, Merging, Rebase, Stash, Tagging
-Git workflows: GitFlow, Feature Branching, Forking
-GitHub: PRs, issues, webhooks, GitHub Actions
-AI Add-on:
AI-powered code reviews (GitHub Copilot, SonarCloud with AI)
Auto-generating commit messages using AI
AI-assisted PR review and bug detection
-Mini project: Git-based team collaboration with AI-assisted PR reviews
Jenkins – CI/CD Automation with AI
-Introduction to CI/CD and Jenkins architecture
-Jenkins setup, plugins, and security
-Jenkins Jobs: Freestyle vs Pipeline
-Jenkinsfile: Scripted vs Declarative
-Webhooks and Git integration
-Jenkins + Maven for Java app build/test
-Setting up Jenkins agents/slaves
-Notifications and post-build actions
-AI Add-on:
AI-powered pipeline optimization (failure prediction & auto-healing)
Automated test case generation using AI
AI integration for anomaly detection in build logs
-Project: Jenkins pipeline with AI-based test validation
Ansible – Configuration Management with AI Assistance
-Why Ansible? Architecture and use cases
-Inventory (static & dynamic), Ad-hoc commands
-YAML basics & writing Playbooks
-Variables, loops, conditionals, handlers
-Roles, Ansible Galaxy, custom modules
-AI Add-on:
AI-assisted playbook generation (ChatGPT-style automation)
Drift detection using AI (compare desired vs. actual infra state)
Predictive automation (suggest configurations based on usage)
-Project: AI-assisted Ansible automation for infra deployment
Terraform – Infrastructure as Code with AI
-Terraform architecture and provider model
-Writing basic .tf files, variables, outputs
-State files, remote state, locking
-Modules for reusable infrastructure
-Terraform lifecycle commands: init, plan, apply, destroy
-Integrating Terraform with Jenkins and Git
-AI Add-on:
AI-assisted Terraform code generation (reduce syntax errors)
Predicting infra costs before provisioning (AI cost analysis)
Auto-remediation of misconfigurations using AI
-Project: Terraform + AI-based cost optimization
Docker – Containerization with AI
-Docker architecture: images, containers, registries
-Docker CLI: build, push, pull, run, exec
-Dockerfile and image optimization
-Volumes, Bind Mounts, Networking
-Docker Compose for multi-container apps
-AI Add-on:
AI-based image optimization (reduce vulnerabilities, shrink image size)
Auto-generation of Dockerfiles using AI tools
Predictive container scaling with AI
-Project: AI-optimized Dockerized full-stack application
Kubernetes – AI-Powered Orchestration
-Kubernetes architecture and core concepts
-Pods, ReplicaSets, Deployments
-Services (ClusterIP, NodePort), Ingress basics
-ConfigMaps and Secrets for app config
-PVCs and persistent storage
-Namespaces, labels, selectors
-Writing YAML files for deployment
-Helm charts basics
-Scaling and rolling updates
-AI Add-on:
AI-based autoscaling & predictive scheduling
AI anomaly detection for cluster health
AI-driven YAML/Helm template generation
-Project: Kubernetes with AI-driven autoscaling and monitoring
Monitoring, Observability & AI (AIOps)
-Prometheus & Grafana setup
-Dashboards and custom alerts
-Log management: ELK Stack (Elasticsearch, Logstash, Kibana)
-Setting up alerting channels (Slack, Email, SNS)
-AI Add-on:
AI anomaly detection in logs & metrics
Predictive alerts instead of reactive monitoring
AI-based RCA (Root Cause Analysis)
-Project: AI-enhanced monitoring dashboard
DevOps Best Practices + AI Future
-Secrets management and security practices
-GitHub/GitLab for code collaboration
-DevOps compliance and auditing
-AI Add-on:
AI in DevSecOps (threat modeling, vulnerability scanning)
AI for compliance (automated audits, compliance drift detection)
AI-assisted mock interviews & code reviews
-Recap with quizzes, mock interviews, and final AI-driven use-case review
- Students or recent graduates
- Quality assurance professionals who want to specialize in Software testing
- Individuals who are looking to transition into a career in software testing
- Software developers who want to expand their skill set and learn more about Software testing
- Project managers Non IT person planning to move to IT
- Experienced Manual Tester Work from Home Aspirants
