AI Spark

AI Spark is a modular, user-friendly framework for developing, training, and deploying AI models. It simplifies AI model management, training processes, and offers various API services to leverage AI capabilities effectively.

Table of Contents

• Features
• Installation
• Usage
  • Training a Model
  • Making Predictions
• Modules
  • ai-spark-core
  • ai-spark-api
• Docker Deployment
• Contributing
• License

Features

• Modular Architecture: Easily extendable and customizable.
• User-friendly API: Simple and intuitive RESTful API endpoints for training and predicting.
• Plugin Support: Add custom AI models and data processing tools with ease.
• Pre-trained Models Integration: Support for popular AI libraries like TensorFlow and Hugging Face.
• Monitoring Tools: Track training processes and model performance.

Installation

Clone the repository and navigate to the project directory:

git clone https://github.com/bayrameker/ai-spark.git
cd ai-spark

Build the project using Maven:

mvn clean install

Usage

Training a Model

To train a new model, use the /api/model/train endpoint. Here is an example using curl:

curl -X POST http://localhost:8080/api/model/train \
-H "Content-Type: application/json" \
-d '{
  "modelType": "neural_network",
  "trainingData": "..."
}'

Making Predictions

To make a prediction with a trained model, use the /api/model/predict endpoint. Here is an example using curl:

curl -X POST http://localhost:8080/api/model/predict \
-H "Content-Type: application/json" \
-d '{
  "modelId": "1234",
  "inputData": [1.0, 2.0, 3.0]
}'

Modules

ai-spark-core

This module contains the core functionalities for AI model training and prediction.

Key Components

• ModelTrainingService: Service for training AI models.
• PredictionService: Service for making predictions with trained models.
• AIModel: Base class for AI models.

ai-spark-api

This module provides RESTful API endpoints for interacting with AI models.

Key Endpoints

• /api/model/train: Train a new model.
• /api/model/predict: Make predictions with a trained model.

Docker Deployment

To deploy AI Spark using Docker, follow these steps:

Build Docker Images

# Core module
cd ai-spark-core
docker build -t ai-spark-core .

# API module
cd ../ai-spark-api
docker build -t ai-spark-api .

Run Docker Containers

docker run -p 8080:8080 ai-spark-core
docker run -p 8081:8081 ai-spark-api

Contributing

Contributions are welcome! Please fork the repository and submit a pull request.

1. Fork the repository.
2. Create a new branch.
3. Make your changes.
4. Submit a pull request.

License

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