DevOps + AWS + Kubernetes + GenAI + AIOps + MLOps
A 12-week program spanning DevOps, AWS, Kubernetes, GenAI-assisted automation, AIOps, MLOps and capstone projects.
What This Course Covers
A 12-week program spanning DevOps, AWS, Kubernetes, GenAI-assisted automation, AIOps, MLOps and capstone projects.
Who Should Attend
Contact Tech Arryaan for audience guidance based on your current experience and the role or technology path you want to pursue.
Prerequisites
Contact our team for prerequisite guidance based on your current experience. Our team can help you choose a suitable starting point.
Approved Source Material
This course page is structured from the supplied DevOps + AWS + Kubernetes + GenAI + AIOps + MLOps Course Content.pdf.
Approved Course Syllabus
Expand each section below to review the approved modules, weeks, projects and learning areas supplied by Tech Arryaan.
- Git & GitHub fundamentals
- GitOps principles
- GitHub Actions pipelines
- Terraform (for AWS infrastructure)
- Ansible for configuration automation
- EC2, IAM, VPC setup
- S3 for storage
- Lambda functions
- CloudWatch for logging
- CodePipeline for automation
- Dockerfile creation
- Pods, Services, Deployments
- Kubernetes YAML configuration
- EKS cluster provisioning with Terraform
- Worker node groups
- IAM roles
- Helm basics
- Integrate GitHub Actions with Helm
- Deploy apps to EKS using CI/CD pipelines
- CloudWatch Anomaly Detection
- DevOps Guru insights
- Lambda-based incident response
- GitHub Copilot for YAML/Terraform/code generation
- Amazon Q Developer for AI-guided automation
- Auto-generate IaC and pipeline configs
- ML lifecycle overview
- MLflow for tracking
- Dockerizing ML models
- DVC for dataset and model versioning
- Training models on SageMaker
- Deploying endpoints
- Model performance tracking
- Prometheus + AlertManager
- CloudWatch Alarms
- Lambda for auto-remediation of K8s pod failures
- End-to-end project integrating DevOps, AIOps, and MLOps components
- E2E ML Deployment via CI/CD — Full pipeline: Train → version → deploy ML model using GitHub Actions & AWS SageMaker
- GenAI DevOps Assistant — Use GitHub Copilot or Amazon Q to auto-generate Terraform + CI/CD YAML configs
- Auto-Healing EKS Infrastructure — Prometheus + CloudWatch alerts trigger Lambda for automatic Kubernetes pod recovery
- Observability + AI — Build real-time dashboards + anomaly detection using CloudWatch and Python scripts
- DevOps: Git, GitHub Actions, Terraform, Ansible
- Cloud: AWS EC2, EKS, IAM, VPC, S3, CloudWatch, CodePipeline, Lambda
- Kubernetes: Helm, Karpenter, kubectl
- AI/GenAI: GitHub Copilot, Amazon Q Developer
- AIOps: AWS DevOps Guru, CloudWatch Anomaly Detection
- MLOps: MLflow, SageMaker, DVC, Docker
Training Information
Training Mode
Confirmed based on the current offering.
Duration
12-Week Training Syllabus
Batch Timing / Next Batch / Trainer
Contact our team for current details.
Practical Learning
Projects, assignments, practical scenarios and capstone items are shown inside the approved syllabus wherever the source document includes them.
Related Technologies
Explore the Cloud & DevOps category for related learning paths.
Course Enquiry
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