MCP + AI Platform on Kubernetes (EKS)
11 & 12 April 2026 · 7:00 PM – 10:00 PM IST (each day) · 2 days
About this session
Deploy a Model Control Plane (MCP) — the brain of modern AI platforms — on AWS EKS with real enterprise architecture. Most courses teach tools. This project teaches you how to build real systems used in production: centralized model management, automated CI/CD pipelines, observability, and enterprise-grade security — the same infrastructure that powers AI platforms at scale.
What you'll learn
Production EKS cluster provisioning with Terraform (VPC, Subnets, IAM, Security Groups)
Control plane design — managing AI models the same way Kubernetes manages containers
Multi-service production architecture with containerized microservices on Kubernetes
AI + DevOps integration (MLOps): model serving APIs and version control for ML models
Model registration, deployment pipelines (staging → production), and metadata tracking
Terraform for full infrastructure automation — IaC best practices at scale
CI/CD pipelines with GitHub Actions / Jenkins for automated builds and deployments
GitOps workflows: Git → CI/CD → EKS with automated sync and rollback
NGINX Ingress / AWS ALB configuration for secure, scalable API exposure
Prometheus metrics scraping and Grafana dashboard configuration
CloudWatch integration for centralized AWS-native log management
IAM Roles for Service Accounts (IRSA) — how real companies secure AI platforms
Kubernetes RBAC policies and network policies for workload isolation
Secrets management via AWS Secrets Manager integrated with Kubernetes
Horizontal Pod Autoscaler (HPA) and Cluster Autoscaler for dynamic scaling
PROJECT WORKFLOW
Webinar agenda
Step 1: Provision production-ready AWS EKS cluster using Terraform (VPC, Subnets, IAM, Security Groups)
Day 1
Step 2: Deploy containerized microservices and model serving APIs on Kubernetes
Day 1
Step 3: Build the Model Control Plane — registration, versioning, and lifecycle management
Day 1
Step 4: Configure IRSA, Kubernetes RBAC, and AWS Secrets Manager for enterprise security
Day 1
Step 5: Build CI/CD pipelines with GitHub Actions / Jenkins and implement GitOps workflows
Day 2
Step 6: Configure NGINX Ingress / AWS ALB for secure public API exposure
Day 2
Step 7: Set up Prometheus metrics, Grafana dashboards, and CloudWatch logging
Day 2
Step 8: Implement Cluster Autoscaler and Horizontal Pod Autoscaler for dynamic scaling
Day 2
Your instructor
Manoj
Senior DevOps & AI Engineer
Agents, RAG, LangGraph, and MCP shipping production AI systems with the same stack taught in our bootcamp.
Who this webinar is for
DevOps Engineers → Move to AI + Platform Engineering
Backend Engineers → Learn Kubernetes + scaling for production systems
ML Engineers → Learn deployment, infrastructure, and production operations
Beginners → Understand real-world architecture that companies actually use
Cloud engineers preparing for Platform Engineering or MLOps roles
Anyone who wants to design systems, scale platforms, and manage AI infrastructure
What's included
2 days of live, hands-on sessions
3 hours each (7–10 PM IST)
Full source code
complete MCP platform on EKS, yours to keep
Terraform configurations for the entire AWS infrastructure
Kubernetes manifests, Helm charts, and CI/CD pipeline definitions
Grafana dashboard configs and Prometheus alerting rules
Register Now
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