The official python package for NimbleBox. Exposes all APIs as CLIs and contains modules to make ML 🌸

A complete production-ready MLOps framework with built-in distributed training, monitoring, and CI/CD. Deploy ML models to production with confidence using our battle-tested infrastructure.

A modular ML pipeline built with Python, scikit-learn, and Docker, featuring YAML-based config management, DVC tracking, CI/CD integration via GitHub Actions, and production-ready FastAPI deployment. Designed for reproducibility, scalability, and monitoring readiness (Prometheus/Grafana).

End-to-end ML platform for Yelp business recommendations and sentiment analysis. Features collaborative filtering (ALS), NLP classification, FastAPI REST API, PySpark data processing, MLflow tracking, Docker deployment, and CI/CD automation. Academic/research project demonstrating production ML engineering.

Developed an image classification web app using CNN to differentiate cats and dogs. Achieved high accuracy, precision, recall, and F1 score. Pipeline involves data preprocessing, model training, Docker deployment on AWS ECS, user-friendly interface, and reliable CI/CD. Showcases deep learning's potential in image analysis.

MLops 5th Sem Project

MLOps

End-to-end MLOps pipeline for hotel booking demand forecasting. Includes modular components for data ingestion, model training, evaluation, versioning, and deployment. Features configuration-based execution, CI/CD with GitHub Actions, and automated logging and testing.

A production-ready MLOps pipeline for detecting melanoma and other skin cancers using AWS SageMaker, with automated retraining, monitoring, and deployment.

An ML system for automated medical signal analysis, evolving from deep learning research to a containerized MLOps pipeline.

Python for MLOps Course

A machine learning project to predict water potability based on quality parameters, featuring an end-to-end MLOps pipeline, a web interface, and scalable deployment with monitoring and CI/CD support.