py-cellar

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.

Repository Structure

The repository is organized into two main components:

1. mlapi: A production-ready ML API template with integrated MLOps components
2. crud_api: A production-ready CRUD API template with authentication

1. mlapi

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

Key Features

• Scalable ML model serving
• Comprehensive logging and monitoring
• Model versioning and metadata management
• Object storage for large files and datasets

2. crud_api

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

Key Features

• RESTful API design
• Database integration with PostgreSQL
• Secure authentication and authorization
• Docker Compose setup for easy deployment
• Comprehensive test suite using pytest

3. Model Registry

4. Feature Store