AI DevOps Engineer Course & Curriculum

NCPL's hands-on AI DevOps Engineer training — full curriculum, real projects, certification prep, and job placement support. We train, mentor and place you.

Module 1: DevOps Fundamentals & AI-Powered Automation

Master DevOps lifecycle with AI-enhanced automation and intelligent scripting.

  • DevOps Lifecycle and the 7 Cs of DevOps
  • CI/CD Pipeline Architecture
  • Git Repository Management & Branching Strategies
  • AI-Assisted Code Reviews and Static Analysis
  • Bash & PowerShell Scripting with AI Copilots
  • Intelligent Script Generation using LLMs
  • GitHub Copilot & AI-Powered IDE Integration

Module 2: AI-Driven CI/CD & Code Quality

Implement intelligent CI/CD pipelines with AI-powered quality gates and testing.

  • SonarQube with AI-Enhanced Code Analysis
  • AI-Powered Test Generation & Coverage Optimization
  • Jenkins & Azure DevOps with AI Plugins
  • Predictive Build Failure Analysis
  • Automated Dependency Vulnerability Scanning
  • AI-Based Release Risk Assessment
  • Intelligent Artifact Management with JFrog

Module 3: Containerization, Kubernetes & MLOps

Master container orchestration and ML model deployment pipelines.

  • Docker Architecture & Multi-Stage Builds
  • Kubernetes for ML Workloads
  • GPU Scheduling & Resource Management
  • MLOps Pipeline Design with Kubeflow
  • Model Serving with KServe & Seldon
  • Feature Store Integration (Feast)
  • ML Model Versioning & Experiment Tracking
  • AKS, EKS & GKE for AI Workloads

Module 4: Infrastructure as Code & AI Cloud Services

Automate AI infrastructure provisioning across multi-cloud environments.

  • Terraform for AI/ML Infrastructure
  • AWS SageMaker Infrastructure Automation
  • Azure ML Workspace Provisioning
  • GCP Vertex AI Infrastructure Setup
  • GPU Cluster Management & Auto-Scaling
  • Cost Optimization for AI Workloads
  • Infrastructure Drift Detection with AI

Module 5: AIOps, Monitoring & Intelligent Observability

Implement AI-powered monitoring, anomaly detection, and self-healing systems.

  • AIOps Fundamentals & Architecture
  • Prometheus & Grafana with AI Anomaly Detection
  • Predictive Alerting & Noise Reduction
  • Log Analysis with ML (ELK + AI)
  • Self-Healing Infrastructure Automation
  • Chaos Engineering with AI-Guided Testing
  • Security Automation & AI Threat Detection
  • Compliance as Code with AI Auditing