AI-Enhanced DevOps & Platform Engineer
CI/CD, Cloud Platforms & Reliability Engineering with AI
Provider
CloudSpace Academy
Duration
20-24 Weeks (Cohort-Based)
Format
Instructor-Led, Live Online + Labs
Level
Intermediate to Advanced
Capstone
End-to-end platform engineering project
Prerequisites
- Cloud fundamentals (AWS preferred)
- Basic Linux and scripting knowledge recommended
Course Overview
The AI-Enhanced DevOps & Platform Engineer program prepares learners to design, build, and operate modern cloud platforms using AI-assisted DevOps workflows.
Rather than focusing only on tools, this program teaches how platform engineers use AI to automate pipelines, observe systems, diagnose issues, and improve reliability at scale.
Graduates leave with hands-on DevOps experience, AI-enabled operational workflows, and portfolio-ready platform projects aligned with real enterprise environments. This is not an entry-level IT program.
Who This Program Is For
- DevOps engineers upgrading to platform roles
- Cloud engineers expanding into operations
- SREs and infrastructure professionals
- Software engineers moving into DevOps
- Veterans and transitioning service members
- This is not an entry-level IT program
Program Outcomes
- Build and manage CI/CD pipelines using AI assistance
- Design internal cloud platforms and developer workflows
- Use AI to analyze logs, metrics, and traces
- Improve system reliability and scalability
- Automate infrastructure and application delivery
- Respond to incidents using structured, AI-assisted workflows
- Communicate operational decisions clearly
Detailed Syllabus
Phase 1
DevOps, Platform Engineering & AI Foundations (Weeks 1-3)
Topics Covered
- Evolution from DevOps to platform engineering
- Modern delivery pipelines
- Role of AI in DevOps and SRE
- Git workflows and collaboration
- Prompting fundamentals for DevOps automation
Outcome
Students understand modern DevOps roles and how AI augments platform operations.
Phase 2
CI/CD Pipelines & Automation (Weeks 4-6)
Topics Covered
- CI/CD principles and pipeline design
- Build, test, and deployment stages
- Pipeline security and guardrails
- AI-assisted pipeline generation and validation
- Failure handling and rollback strategies
Outcome
Students can design and automate CI/CD pipelines with AI support.
Phase 3
Containerization & Platform Foundations (Weeks 7-9)
Topics Covered
- Containers and container orchestration concepts
- Kubernetes fundamentals (platform perspective)
- Platform abstractions and self-service models
- AI-assisted configuration analysis
Outcome
Students can operate containerized platforms and reason about system design.
Phase 4
Infrastructure as Code & Environment Management (Weeks 10-13)
Topics Covered
- Infrastructure as Code principles
- Terraform and/or CloudFormation
- Environment consistency and promotion
- AI-assisted IaC generation and review
- Change management and drift detection
Outcome
Students can automate infrastructure provisioning and lifecycle management.
Phase 5
Observability, Reliability & Incident Response (Weeks 14-17)
Topics Covered
- Observability pillars: logs, metrics, traces
- Monitoring strategies and alerting
- Reliability engineering concepts
- Incident response workflows
- AI-assisted root cause analysis
Outcome
Students can detect, diagnose, and respond to system issues efficiently.
Phase 6
Cost Optimization, Security & Platform Governance (Weeks 18-20)
Topics Covered
- Cloud cost models and optimization strategies
- Platform security basics
- Policy enforcement and guardrails
- AI-assisted cost and risk analysis
- Platform performance optimization
Outcome
Students can balance reliability, cost, and security in platform design.
Phase 7
Capstone Project (Weeks 21-24)
Capstone Requirements
Design an internal developer platform on AWS, implement CI/CD pipelines, automate infrastructure provisioning, implement observability and alerting, and use AI tools for troubleshooting and optimization.
Final Deliverables
- Architecture diagrams
- Deployed platform environment
- Incident response walkthrough
- Portfolio-ready platform case study
AI-Enhanced Workflows Taught
Throughout the program, students learn how to:
- Generate and validate pipelines with AI
- Analyze logs and metrics using AI reasoning
- Accelerate incident diagnosis
- Improve documentation and runbooks
- Support platform decisions with AI insights
- Use AI as a productivity and reliability multiplier
Final Graduation Outcomes
Graduates leave with:
- Real-world DevOps and platform experience
- AI-enabled operational workflows
- Production-style platform projects
- Confidence to operate modern systems
- Readiness for DevOps, SRE, and platform roles