AI Automation for DevOps Teams

Aim

Program Objectives

Program Structure

🗓️ Week 1: Foundations of AI‑Driven DevOps
Module 1: DevOps Lifecycle & AI Integration

• Chapter 1.1: DevOps Stages Refresher: CI/CD, Monitoring, IaC

• Chapter 1.2: AI/ML Essentials for Ops Teams

• Chapter 1.3: AI Opportunities Across DevOps

  • Use cases from code to postmortem

Module 2: Toolchains & Architectures

• Chapter 2.1: GitHub Copilot, AIOps Tools, MLflow

• Chapter 2.2: Designing AI-Ready DevOps Architectures

• Chapter 2.3: Enabling Data Flow for ML Models in DevOps

🗓️ Week 2: Code, Testing & Deployment Automation
Module 3: Code Intelligence & CI/CD Acceleration

• Chapter 3.1: AI-Driven Code Review & Security Analysis

• Chapter 3.2: Smart Testing & Flaky Test Detection

• Chapter 3.3: Predictive Builds and Release Optimizations

Module 4: AIOps in Monitoring & Response

• Chapter 4.1: Anomaly Detection in Logs & Metrics

• Chapter 4.2: Root Cause Analysis and Correlation

• Chapter 4.3: Auto-Remediation and Intelligent Alerts

🗓️ Week 3: Infrastructure, Governance & Case Studies
Module 5: Smart Infrastructure and Scaling AI Ops

• Chapter 5.1: AI for IaC (Terraform, Pulumi)

• Chapter 5.2: Drift Detection & Self-Healing Templates

• Chapter 5.3: AI Predictions for Scaling and Failover

Module 6: Ethics, Governance & Real-World Impact

• Chapter 6.1: AI Explainability in Ops Contexts

• Chapter 6.2: Policy-as-Code with AI Enforcement

• Chapter 6.3: Industry Case Studies + Roadmap Planning

Who Should Enrol?

Program Outcomes

Fee Structure

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What You’ll Gain