AI Cloud DevOps
AIOps, Generative AI & Machine Learning Operations
Master the intersection of AI, cloud computing and DevOps with an advanced AIOps and AI automation curriculum designed for working professionals. Learn how to use AWS AI services, Docker, Kubernetes and generative AI to supercharge your DevOps pipelines or upgrade from our AWS Cloud course and Azure Cloud course.
AI Cloud DevOps Course Overview
This advanced AI Cloud DevOps course bridges traditional DevOps with modern AIOps and generative AI practices for real-time operations. You will learn how to use AI to detect anomalies, predict incidents, auto-generate pipelines and infrastructure code, and optimize cost and performance across AWS. The program is ideal after completing our DevOps Engineer course or any cloud course when you want to specialize in AIOps and AI automation.
Technology Ecosystem Covered
Cloud Platforms
AWS AI services, Azure AI and Google Vertex AI
AIOps & MLOps Tools
Kubeflow, Prometheus, Grafana, MLflow, W&B
AI & Generative AI
Amazon Bedrock, CodeWhisperer, LLMs for DevOps
DevOps & Cloud Native
Docker, Kubernetes, GitOps, CI/CD automation
AI Cloud DevOps Key Highlights
AI & AIOps Experts
Learn directly from engineers who work on AIOps, incident prediction and AI-powered monitoring in real environments.
Future-Ready Roles
100% placement assistance for AI DevOps, AIOps Engineer and AI Platform Engineer roles with dedicated support.
Enterprise Projects
Build production-style AIOps pipelines, AI monitoring dashboards and GenAI-powered CI/CD automation.
Multi-Cloud Mastery
Learn patterns that apply across AWS so you can work as a cloud-agnostic AI DevOps engineer.
Premium Value
Focus on high-paying AIOps and AI platform roles with a compact, intensive 15-day program.
Flexible Learning
Weekend and weekday hybrid options that work with your existing cloud or DevOps projects.
No Cost EMI
Easy EMI plans so you can pair this with your existing AWS, Azure or DevOps learning path.
AIOps Specialization
Get a niche specialization in AIOps and AI-powered IT operations on top of your DevOps skills.
Complete AI Cloud DevOps Syllabus
Practical AI Cloud DevOps curriculum covering AI on AWS, GenAI, AIOps, AI-powered CI/CD and real interview-focused projects.
AI in AWS for DevOps
- Understand how AI integrates with AWS-based DevOps workflows
- Learn Amazon Bedrock to deploy AI models without managing infrastructure
- Build, train and deploy ML models with Amazon SageMaker
- Use Amazon CodeWhisperer to auto-suggest code and improve quality
- AI-powered monitoring and optimization on AWS (CloudWatch, DevOps Guru)
- Use cases: predictive scaling, anomaly detection, AI-driven cost optimization
- Apply AI to automate performance tuning and security alerts
- Hands-on labs: Bedrock, SageMaker and CodeWhisperer in a DevOps pipeline
AI-Powered IDE & Developer Productivity
- Overview of AI-assisted development tools and IDEs
- Working with Windsurf IDE for easy code build in DevOps projects
- Using Windsurf for code navigation, refactoring and reviews
- Automate code reviews, linting and security scans with AI tools
- Integrating AI suggestions into Git-based workflows (GitHub/GitLab)
- Best practices for using AI assistance without losing code quality
- Prompt engineering for developer tools and code assistants
- Hands-on: AI pair-programming for CI/CD and IaC code
AI with Docker & Kubernetes
- Building AI-ready Docker images for inference and training
- Using AI to generate and simplify Dockerfile logic
- Optimizing container images for GPU and CPU workloads
- Introduction to Crane by Tencent for container operations
- Running AI workloads on Kubernetes clusters
- Kubeflow as an end-to-end MLOps platform on Kubernetes
- Monitoring AI workloads with Prometheus + Grafana + AI plugins
- Hands-on: containerizing an AI service and deploying on K8s
AIOps – Smart IT Operations
- What is AIOps and how it extends traditional monitoring
- Detect anomalies in logs, metrics and system health using AI
- Incident prediction and auto-remediation with machine learning
- Using AI for capacity planning and demand forecasting
- AI-based Root Cause Analysis (RCA) to reduce MTTR
- Integrating AIOps with existing tools (Prometheus, Grafana, ELK, CloudWatch)
- Designing alert strategies with AI-driven noise reduction
- Hands-on: anomaly detection and RCA for a sample production stack
Generative AI for DevOps Automation
- Overview of LLMs and their role in DevOps automation
- Use LLMs to auto-generate infrastructure-as-code (Terraform, CloudFormation, ARM)
- Automate shell scripts and routine admin tasks using AI assistants
- Generate CI/CD pipeline YAMLs (Jenkins, GitLab, GitHub Actions) using AI
- AI-generated runbooks and standard operating procedures (SOPs) for incidents
- ChatOps with Generative AI using Slack / MS Teams
- Security and governance considerations for GenAI in DevOps
- Hands-on: build an end-to-end pipeline spec using a GenAI assistant
AI-Enhanced Monitoring & Observability
- Traditional vs AI-driven observability for cloud-native systems
- Using AI to correlate logs, metrics and traces automatically
- Anomaly detection models for performance and reliability issues
- AI plugins for Prometheus and Grafana dashboards
- Predictive alerts and early warning signals for outages
- AI-based cost, performance and SLO optimization recommendations
- Designing feedback loops between AI models and monitoring tools
- Hands-on: build an AI-assisted observability dashboard
AI-Driven CI/CD & IaC
- Using AI to bootstrap new microservice repositories and pipeline templates
- Auto-generating Terraform / CloudFormation modules from natural language
- LLM-assisted creation of Jenkins and GitLab CI/CD YAMLs
- AI validation of pipeline changes, policies and security checks
- Integrating AI checks into pull requests and code review workflows
- Generating rollback and hotfix plans with AI guidance
- Designing AI-assisted governance for CI/CD at scale
- Hands-on: build a CI/CD pipeline faster using AI suggestions
AI Ops – Automation & Self-Healing
- Self-healing patterns for cloud and Kubernetes workloads
- Defining policies and triggers for AI-based auto-remediation
- Using ML models to recommend remediation actions
- Closed-loop incident management with AI playbooks
- Combining runbooks + LLMs for guided troubleshooting
- Measuring impact of AIOps (downtime reduction, MTTR, SLOs)
- Case studies: AIOps implementations in large enterprises
- Hands-on: build a basic self-healing workflow for a sample app
Real-World AI Cloud DevOps Projects
- Design and implement an AI-enhanced CI/CD pipeline on AWS
- Deploy AI-powered monitoring and anomaly detection for a microservices app
- Use Generative AI to create IaC, scripts and SOPs for a new environment
- Implement AIOps incident prediction and automated RCA for logs/metrics
- Integrate AI chatbots for ChatOps with Slack / MS Teams
- Build an end-to-end MLOps flow using Docker, Kubernetes and Kubeflow
- Document and present project architecture and decisions
- Prepare GitHub portfolio with AI Cloud DevOps project artifacts
Interview Preparation & Career Readiness
- Real-time AI + Cloud + DevOps scenario questions and whiteboard tasks
- Agile methodology, sprint planning and change execution methods
- Day-to-day activities for AI Cloud DevOps and AIOps engineers
- Troubleshooting patterns for production incidents and outages
- Weekly meets with placed students and active job seekers for peer learning
- Improving communication skills to explain technical work in interviews
- Building a strong resume and LinkedIn profile for AI Cloud DevOps roles
- Career roadmap and continuous upskilling plan in AI, cloud and DevOps
AI Cloud DevOps FAQs
AI Cloud DevOps Course Features
| Level | Advanced/Expert |
| Class Type | Virtual & Hybrid |
| Duration | 15 Days |
| Lectures | 20 |
| Labs/Projects | 20 |
| Total Sessions | 15 |
| Certificate | Yes |
| Daily Test | 1 daily Test |
| Weekly Test | 1 weekly Test |
| Mock Test | 7 mock Test |
Additional Benefits
Shape the Future with AI DevOps
Upgrade your profile from cloud or DevOps engineer to AI Cloud DevOps specialist with AIOps, generative AI and real-time incident automation skills, or compare with our DevOps course before you decide.
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