py-cellar is a comprehensive repository that encompasses Machine Learning (ML), MLOps, and DataOps codes,works,notes. This repository provides production-ready templates and tools for building robust ML applications and data pipelines.
The repository is organized into two main components:
- mlapi: A production-ready ML API template with integrated MLOps components
- crud_api: A production-ready CRUD API template with authentication
The mlapi directory contains a production ML API template along with essential MLOps components. It's designed to be deployed using Docker Compose and includes the following services:
- ML API: A FastAPI-based service for serving machine learning models
- MongoDB: For storing operation logs, model metadata, and model results
- Prometheus: For monitoring and alerting
- Grafana: For creating dashboards and visualizing metrics
- MinIO: Object storage for model artifacts and large datasets
- Scalable ML model serving
- Comprehensive logging and monitoring
- Model versioning and metadata management
- Object storage for large files and datasets
The crud_api directory contains a production-ready CRUD API template with authentication. It's designed to be deployed using Docker Compose and includes:
- CRUD API: A FastAPI-based service for handling CRUD operations
- PostgreSQL: As the primary database for storing application data
- Authentication Service: For secure user authentication and authorization
- Pytest Integration: For comprehensive unit and integration testing
- RESTful API design
- Database integration with PostgreSQL
- Secure authentication and authorization
- Docker Compose setup for easy deployment
- Comprehensive test suite using pytest