AWS Examples & Experiments

Note: This repository is archived and no longer actively maintained.

A comprehensive collection of AWS examples, experiments, and learning materials covering various AWS services including AI/ML, Lambda, ECS, IoT, and more.

📋 Table of Contents

• Overview
• Project Structure
• Getting Started
• Services Covered
• Contributing
• License

🎯 Overview

This repository contains practical examples and experiments with AWS services, organized by service category. Each subfolder contains specific implementations, tutorials, and code samples for learning and reference purposes.

📁 Project Structure

AI & Machine Learning

• AmazonAI/ - Amazon AI services (Polly, Rekognition, Comprehend, Translate)
• ML/ - Machine learning examples (scikit-learn, Spark, Mahout, Amazon ML)

Compute & Serverless

• lambda_frameworks/ - Lambda frameworks and examples
• ecs/ - ECS (Elastic Container Service) examples
• edge/ - Edge computing and Lambda@Edge

Data & Analytics

• athena/ - Amazon Athena examples and queries
• redshift/ - Amazon Redshift examples
• bigdatabattle/ - Big data processing examples

Infrastructure & DevOps

• CF/ - CloudFormation and infrastructure as code
• iot/ - IoT examples and implementations

Frameworks & Tools

• mxnet/ - MXNet deep learning examples
• javabackends/ - Java backend examples
• dsstne/ - Deep Scalable Sparse Tensor Network Engine

Data Pipelines

• serverlessPipeline/ - Serverless data pipeline examples
• sparkStreamingFromS3/ - Spark streaming from S3

🚀 Getting Started

1. Clone the repository:

   git clone https://github.com/yourusername/aws.git
   cd aws
   

2. Navigate to specific service examples:

   • For Lambda examples: cd lambda_frameworks/
   • For AI services: cd AmazonAI/
   • For ML examples: cd ML/

3. Follow individual README files in each subfolder for specific setup instructions.

🔧 Services Covered

📚 Learning Path

1. Beginner: Start with lambda_frameworks/ for serverless basics
2. Intermediate: Explore AmazonAI/ for AI service integration
3. Advanced: Dive into ML/ and mxnet/ for machine learning
4. Expert: Work with serverlessPipeline/ for complex architectures

🤝 Contributing

Note: This repository is archived and no longer accepting contributions.

If you find this repository useful, please consider:

• ⭐ Starring the repository
• 📖 Using the examples for your own learning
• 🔗 Sharing with others who might benefit

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

⚠️ Disclaimer

• These examples are for educational purposes
• Some examples may use older AWS SDK versions
• Always review and test code before using in production
• AWS services and APIs may have changed since these examples were created

Last updated: 2024
Status: Archived - No longer actively maintained