Terraform Bootcamp: AWS Infrastructure for Web Apps & ML/AI
Build production-ready AWS infrastructure from scratch using Terraform. In 12 hands-on lessons, deploy a complete web application stack — VPC, EC2, RDS, S3, IAM — then extend it with ML/AI infrastructure including SageMaker, GPU instances, and model serving endpoints. Bilingual content (English + 中文). No prior Terraform or cloud experience required.
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Lesson Overview
Lesson 1: What is Infrastructure as Code?
Understand why Infrastructure as Code exists, compare leading IaC tools, and meet the NomadEats startup scenario we will build throughout this bootcamp.
Lesson 2: Installing Terraform & Your First Resource
Install Terraform and the AWS CLI, configure credentials, and create your first S3 bucket using the full init/plan/apply/destroy lifecycle.
Lesson 3: State, Variables, and Outputs
Learn how Terraform state tracks your infrastructure, use variables to make configurations reusable, and define outputs to surface useful information after deployments.
Lesson 4: Building the VPC & Networking
Design and provision a complete AWS VPC with public and private subnets, an Internet Gateway, a NAT Gateway, and route tables using Terraform.
Lesson 5: Security Groups — Your Cloud Firewall
Create stateful firewall rules for NomadEats using Terraform security groups with least-privilege ingress and egress rules.
Lesson 6: Launching EC2 Instances
Provision EC2 instances with SSH key pairs, AMI data sources, user data bootstrapping, and Elastic IPs using Terraform.
Lesson 7: RDS — Managed PostgreSQL Database
Provision a managed PostgreSQL database in private subnets with secure password generation, DB subnet groups, and Multi-AZ high availability.
Lesson 8: S3 for Static Assets & File Uploads
Create and configure S3 buckets with versioning, lifecycle rules, bucket policies, public access blocks, and CORS for the NomadEats platform.
Lesson 9: IAM Roles, Policies & Instance Profiles
Build IAM roles with trust policies, attach fine-grained S3 permissions, and connect EC2 instances to AWS services securely using instance profiles.
Lesson 10: Terraform for ML: GPU Instances & SageMaker
Provision GPU compute instances, SageMaker notebooks, and S3 storage for NomadEats ML training workflows using Terraform.
Lesson 11: Model Serving & ML Pipelines on AWS
Deploy trained ML models for real-time inference using SageMaker endpoints, Lambda functions, and API Gateway with full Terraform automation.
Lesson 12: Modules, Environments & Production Readiness
Refactor the NomadEats infrastructure into reusable Terraform modules, manage multiple environments with workspaces, and establish CI/CD pipelines and monitoring for production.