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

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.
• State Management: Terraform maintains a state file that tracks all resources it manages. This helps Terraform understand what resources exist and how to modify them when configurations change.
• Plan and Apply: Terraform follows a two-step process:
  • First, it creates an execution plan showing what changes it will make
  • Then, after approval, it applies those changes to create or modify the infrastructure

For our medical call analysis system, Terraform will automate the deployment of several key AWS resources. This includes creating S3 buckets for storing audio files and transcripts, deploying Lambda functions with appropriate permissions and configurations, and setting up IAM roles and policies to ensure secure access to AWS services. We'll also configure CloudWatch for comprehensive monitoring and logging, allowing us to maintain visibility into our system's performance. One of the biggest advantages of using Terraform is its ability to manage all these resources in a version-controlled, repeatable way, making it easy to spin up or tear down complete environments for testing and scaling.

Let's dive into how we can use this incredible tool to automate the deployment of all the AWS resources needed for our medical call analysis system!

Project Structure and Prerequisites

Before getting started with this tutorial, you'll need to set up your development environment. This includes having the AWS CLI configured with appropriate credentials and Python 3.11 installed for Lambda development. You'll also need an AWS account with the necessary permissions to create resources. To authenticate the Terraform AWS provider, you'll need to set up your IAM credentials by exporting your AWS access key ID and secret key as environment variables: $ export AWS_ACCESS_KEY_ID= $ export AWS_SECRET_ACCESS_KEY=

Install Terraform

To install Terraform, you can download it as a binary package from HashiCorp's website or use popular package managers. The installation process involves downloading the appropriate package for your system as a zip archive, extracting it to get the single terraform binary, and adding it to your system's PATH.