2150038862

AI Automation for DevOps Teams

Empower DevOps with Intelligent Automation: Faster Releases, Smarter Workflows.

MODE
Mode(Online) TYPE
Mentor Based LEVEL
Moderate

Skills you will gain:

The “AI Automation for DevOps Teams” program is designed to bridge the gap between traditional DevOps practices and emerging AI-driven automation. This course empowers DevOps engineers, SREs, and operations teams to integrate artificial intelligence and machine learning into their workflows. Participants will explore AI-based predictive maintenance, anomaly detection, intelligent alerting, automated testing, deployment automation, and more.

Through real-world scenarios and hands-on activities, this program fosters deep understanding of AI applications in DevOps, enabling teams to scale, optimize, and secure their infrastructure more intelligently.

Aim:

To equip DevOps professionals with cutting-edge AI and automation skills to streamline CI/CD pipelines, improve infrastructure monitoring, and boost team efficiency through intelligent automation strategies.

Program Objectives:

  • Introduce the core principles of AI-augmented DevOps

  • Equip learners with hands-on experience in intelligent automation

  • Enable the development and deployment of ML models within DevOps pipelines

  • Build readiness for next-generation DevOps job roles

  • Foster continuous learning in AIOps and platform engineering

What you will learn?

🗓️ 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

Intended For :

  • DevOps Engineers

  • Site Reliability Engineers (SREs)

  • Infrastructure Engineers

  • Software Developers transitioning into DevOps

  • AI/ML Engineers exploring DevOps

  • Final-year students and postgraduates in Computer Science/IT

Career Supporting Skills

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