AI Automation for DevOps Teams
Empower DevOps with Intelligent Automation: Faster Releases, Smarter Workflows.
Early access to e-LMS included
About This Course
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
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Introduce the core principles of AI-augmented DevOps
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Equip learners with hands-on experience in intelligent automation
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Enable the development and deployment of ML models within DevOps pipelines
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Build readiness for next-generation DevOps job roles
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Foster continuous learning in AIOps and platform engineering
Program Structure
🗓️ Week 1: Foundations of AI‑Driven DevOps
Module 1: DevOps Lifecycle & AI Integration
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Chapter 1.1: DevOps Stages Refresher: CI/CD, Monitoring, IaC
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Chapter 1.2: AI/ML Essentials for Ops Teams
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Chapter 1.3: AI Opportunities Across DevOps
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Use cases from code to postmortem
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Module 2: Toolchains & Architectures
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Chapter 2.1: GitHub Copilot, AIOps Tools, MLflow
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Chapter 2.2: Designing AI-Ready DevOps Architectures
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Chapter 2.3: Enabling Data Flow for ML Models in DevOps
🗓️ Week 2: Code, Testing & Deployment Automation
Module 3: Code Intelligence & CI/CD Acceleration
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Chapter 3.1: AI-Driven Code Review & Security Analysis
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Chapter 3.2: Smart Testing & Flaky Test Detection
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Chapter 3.3: Predictive Builds and Release Optimizations
Module 4: AIOps in Monitoring & Response
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Chapter 4.1: Anomaly Detection in Logs & Metrics
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Chapter 4.2: Root Cause Analysis and Correlation
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Chapter 4.3: Auto-Remediation and Intelligent Alerts
🗓️ Week 3: Infrastructure, Governance & Case Studies
Module 5: Smart Infrastructure and Scaling AI Ops
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Chapter 5.1: AI for IaC (Terraform, Pulumi)
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Chapter 5.2: Drift Detection & Self-Healing Templates
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Chapter 5.3: AI Predictions for Scaling and Failover
Module 6: Ethics, Governance & Real-World Impact
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Chapter 6.1: AI Explainability in Ops Contexts
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Chapter 6.2: Policy-as-Code with AI Enforcement
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Chapter 6.3: Industry Case Studies + Roadmap Planning
Who Should Enrol?
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DevOps Engineers
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Site Reliability Engineers (SREs)
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Infrastructure Engineers
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Software Developers transitioning into DevOps
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AI/ML Engineers exploring DevOps
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Final-year students and postgraduates in Computer Science/IT
Program Outcomes
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Automate CI/CD pipelines using AI agents
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Deploy predictive monitoring systems using ML models
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Implement smart alerting and reduce false positives
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Build self-healing infrastructure components
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Increase deployment frequency and reduce mean time to resolution (MTTR)
Fee Structure
Discounted: ₹21499 | $249
We accept 20+ global currencies. View list →
What You’ll Gain
- Full access to e-LMS
- Real-world dry lab projects
- 1:1 project guidance
- Publication opportunity
- Self-assessment & final exam
- e-Certificate & e-Marksheet
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