Empower DevOps with Intelligent Automation: Faster Releases, Smarter Workflows.
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.
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.
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
🗓️ 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
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
Automate CI/CD pipelines using AI agents
Deploy predictive monitoring systems using ML models
Implement smart alerting and reduce false positives
Build self-healing infrastructure components
Increase deployment frequency and reduce mean time to resolution (MTTR)
Discounted: ₹21499 | $249
We accept 20+ global currencies. View list →
Yes. Paid participants get the session recordings and study material in their NanoSchool dashboard (My registrations → Recorded Lectures / Study Material).
Watch the recording in your dashboard and send your questions to the mentor through the support desk.
Yes, an e-certificate of participation after the workshop, with a QR code anyone can verify and an "Add to LinkedIn" button.
Choose the category that matches your current role: Student, Ph.D. Scholar / Researcher, Academician / Faculty or Industry Professional.
By card, UPI or PayPal on the payment page; international bank transfer is also available there.
See our Returns, Refunds & Cancellation policy: Returns, Refunds & Cancellation policy
Registration open
Live, mentor-led and certified. Dates and times in IST.
with Abhimanyu
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