Github Repo: https://github.com/pedropcamellon/medical-calls-analysis-aws

Summary

Terraform as Infrastructure as Code: Automates AWS resource deployment with declarative code, eliminating manual configuration and ensuring consistency.
Improved Deployment Process: Makes infrastructure deployments consistent, efficient, and scalable while reducing human error.
Structured Project Organization: Uses dedicated files for configuration (main.tf), variables (variables.tf), and outputs (outputs.tf) to maintain clean, modular code.
Security-Focused IAM Policies: Implements least-privilege access control for Lambda functions with precise S3, Transcribe, and Bedrock permissions.
Event-Driven Architecture: Leverages S3 notifications to create a decoupled serverless pipeline that independently triggers transcription and summarization functions.
Environment Management: Supports multiple environments (development, staging, production) through workspaces with environment-specific variables.
Resource Lifecycle Control: Provides complete infrastructure lifecycle management from creation to destruction with simple commands.

Introduction

In the previous post of our series, we explored the importance of monitoring and logging in AI applications using CloudWatch. Now, we'll take a step further by introducing Terraform, an Infrastructure as Code (IaC) tool, to automate the deployment of our serverless architecture.

Terraform, developed by HashiCorp, is an open-source tool that lets you define and provision cloud infrastructure using HCL (HashiCorp Configuration Language), a declarative configuration language. As a powerful Infrastructure as Code (IaC) solution, it moves beyond traditional scripts and web interfaces—you simply describe what you want your infrastructure to look like, and Terraform determines how to make it happen.

Manually configuring AWS resources through the Console can be tedious, error-prone, and hard to scale. Terraform lets you define your entire infrastructure in code, making deployments:

Consistent: Infrastructure definitions are version-controlled and repeatable, reducing configuration drift. When connected to version control systems like GitHub or GitLab, HCP Terraform can automatically propose infrastructure changes based on your code commits.
Efficient: Changes are tracked, reviewed, and applied automatically, minimizing manual errors.
Scalable: Easily replicate environments for development, staging, or production with minimal effort.

Key concepts:

Declarative Language: You specify what infrastructure you want (the "desired state") rather than writing step-by-step instructions on how to create it. Terraform figures out the necessary steps to achieve that state.
Providers: These are plugins that enable Terraform to interact with various cloud platforms and services. The AWS provider, which we'll use, allows Terraform to create and manage AWS resources.