Amazon AWS

Tutorial

AI Assistant with AWS Bedrock and Go

Develop an AI assistant by combining AWS Bedrock, Go, and React for a dynamic frontend.

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Building the Backend

Configuring a New Project in GoLand.

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Getting Started

Preparing your environment.

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Introduction

What exactly is Amazon Bedrock?

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Interacting with ML Models — Llama3

Interacting with Llama3 in AWS Bedrock.

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Interacting with ML Models — Anthropic

Interacting with Anthropic in AWS Bedrock.

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References

Code Samples and Reference Materials

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Enable Model Access

Turning On Models in AWS Bedrock.

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Authentication

Integrating FastAPI with JWT Tokens.

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Cleanup

Cleaning up stack (removing RDS, EKS, ElastiCache, LoadBalancer, Route53).

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Project Setup

Setting up FastAPI project type in PyCharm Professional.

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Elastic Kubernetes Service (EKS)

Deploying application using EKS, RDS, ElastiCache, Route53 & AWS Certificate Manager.

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Celery & Redis

Configuring Celery & Redis with FastAPI.

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REST API - Part I

Implementing a REST API for User modules using Pydantic & API Router.

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Kubernetes Deployment

Writing K8s manifests & deploying in minikube.

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Helm Charts

Combine your K8s manifests into a single package using Helm Charts.

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References

Reference materials which helped to prepare this tutorial.

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REST API - Part II

Performing CRUD operations in our User module.

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REST API - Part III

Performing CRUD operations in Products & Category Module.

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REST API - Part IV

Performing CRUD operations in Orders & Cart Module.

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REST API - Part V

Performing CRUD operations in Orders along with placing a new order.

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Database Setup - Part I

Configuring database connection with SQLAlchemy and FastAPI.

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Database Setup - Part II

Setting up database migrations with Alembic.

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Testing

Writing Unit Tests using Pytest & Asyncio.

Tutorial

Developing FastAPI Application using K8s & AWS

Build seamless FastAPI applications in PyCharm through Kubernetes & AWS.

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Introduction

Introducing FastAPI, a modern, fast (high-performance), web framework.

Tutorial

Developing Django Application using AWS

Run, build, test and deploy Django Application in PyCharm using NICE DCV.

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Behavior-Driven Development(BDD) Testing

Writing and running BDD tests using Behave.

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Cleanup

Cleaning up resources from AWS.

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GitHub Actions

Deploy Docker Image to AWS ECR through GitHub Actions.

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Exploring Django with Docker

Building and running Django Containers through Docker.

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Exploring Project Structure & Creating Django App

Create a new app, define models, and explore the project's structure.

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Introduction

Introducing Django, a high-level python web framework.

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Creating a new Django Project in PyCharm

Create a virtual environment and install Python dependencies.

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Secure REST APIs with JWT

Using Simple JWT authentication plugin for the Django REST Framework.

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Implementing Rest APIs

Writing CRUD (Create, Read, Update, Delete) in Django Rest Framework.

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Setup Ubuntu Desktop in AWS using NICE DCV

Configure security group and launch a new instance.

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Unit Tests

Writing and running unit tests in Django.

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Setup AWS RDS in PyCharm

Connect Postgres with our Django Application.

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Setting up VPC and RDS in AWS

Setup VPC, Subnets, Route Tables, Internet Gateway and Relational Database.

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Integrating Sentry Application Monitoring

Track errors & monitor performance in all major languages & frameworks with Sentry.

Tutorial

Developing Serverless APIs using AWS Toolkit

Run, build, and deploy serverless functions in PyCharm.

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Setting Up a Database in MongoDB Atlas

Connect MongoDB smoothly by connecting through Pycharm Professional to explore the database GUI.

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Securing APIs with JSON Web Tokens (JWT)

Adding Custom Authorizers in Lambda functions

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Deploying Functions

Use PyCharm Professional to deploy your Lambda APIs.

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Background on Serverless

Introduce Microservices, AWS SAM (Serverless Application Model)

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Writing REST APIs

Beginning CRUD (Create, Read, Update, Delete) Operations

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Exploring Project Structure

Launching up project in PyCharm Professional, installing plugin and exploring the project layout.

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Invoking Functions Locally

Execute Lambda function inside a container through PyCharm Professional.

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Cleanup

Removing Resources from AWS Console

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Project Setup

Install AWS SAM (Serverless Application Model), AWS CLI & finally PyCharm Plugin AWS Toolkit.

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Testing

Writing unit tests of our functions that we deploy to AWS Lambda, to ensure everything works.