AIOrchestra is a microservice-based application designed to provide personalized song recommendations using a custom-trained machine learning (ML) model. This project focuses on learning and implementing advanced technologies, including microservice architecture, event-driven systems, and machine learning.
AIOrchestra leverages a scalable and efficient event-driven architecture with microservices to deliver personalized song recommendations. It is built using .NET Core, Kafka, Docker, and TensorFlow, making it an ideal project for learning and practicing these technologies.
- Microservice Architecture: Independent, scalable services for different functionalities.
- Event-Driven Architecture: Utilizes Kafka for asynchronous communication between services.
- Machine Learning: Custom-trained model with TensorFlow for generating song recommendations.
- Docker: Containerized services for consistent deployment and management.
- Database Synchronization: Decoupled services with their own databases and synchronization mechanisms.
- .NET Core 8: Framework for building microservices.
- Kafka: Event streaming platform for managing communication between services.
- Docker: Containerization for deployment consistency.
- TensorFlow: Machine learning framework for building and training the recommendation model.