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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.
PhD in Computational Mechanics from MIT with 15+ years of experience in Industrial AI. Former Lead Data Scientist at Tesla and current advisor to Fortune 500 manufacturing firms.
Professional Certification Program
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
📽️ Week 1 Videos (~6.5 hours)
DevOps Lifecycle 101 (25 min)
Intro to AI for DevOps (30 min)
Use Cases Deep Dive (30 min)
AIOps Tools Tour (30 min)
Data Enablement for Models (30 min)
Building AI-Ready Architectures (35 min)
Summary & Quiz Review (30 min)
📽️ Week 2 Videos (~7 hours)
Code Review with AI Demo (30 min)
Security Scan Bots (30 min)
Smart Testing Explained (45 min)
Predictive Build Failures (40 min)
RCA Techniques with ML (30 min)
Auto-Alert Generation (30 min)
End-to-End CI/CD Pipeline Example (45 min)
📽️ Week 3 Videos (~6.5 hours)
IaC Optimization with AI (30 min)
Predictive Scaling Models (30 min)
Compliance via Policy-as-Code (30 min)
DevOps Ethics in AI Usage (30 min)
Real-World DevOps AI Case Study (45 min)
Building Your AI DevOps Roadmap (45 min)
Final Capstone Walkthrough (30 min)
🎤 Lecture 1 (Week 1)
Title: Laying the AI Foundation in DevOps
Duration: 60 minutes
Guest: DevOps AI Architect
Interactive: Toolchain Walkthrough & Q/A
🎤 Lecture 2 (Week 2)
Title: Pipeline Intelligence: Smarter Builds and Faster Tests
Duration: 75 minutes
Guest: DevOps Automation Specialist
Interactive: Real-world CI/CD Pipeline Debug
🎤 Lecture 3 (Week 3)
Title: Future of AI in DevOps Teams
Duration: 90 minutes
Guest Panel: SRE, Cloud AI Lead, Compliance Officer
Interactive: Governance Scenario Workshop + Capstone Review
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)
Fee: INR 21499 USD 249
We are excited to announce that we now accept payments in over 20 global currencies, in addition to USD. Check out our list to see if your preferred currency is supported. Enjoy the convenience and flexibility of paying in your local currency!
List of Currencies
AI DevOps Engineer
Site Reliability Engineer (SRE) with AI specialization
Automation Architect
Infrastructure Intelligence Engineer
Platform Engineer (ML Ops/DevOps)
DevOps Consultant with AI integration skills
Cloud-based product companies (AWS, Azure, GCP ecosystems)
AI-first startups automating DevOps
Large enterprises digitizing IT operations (AIOps)
SaaS companies and DevSecOps roles
Automation and Monitoring tool vendors (e.g., Datadog, Splunk, Dynatrace)
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