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

A cloud-based Forex forecasting platform using machine learning to predict currency price movements in real time, built with strong software engineering and MLOps best practices for scalable data pipelines, APIs, and production deployment.

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.

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.

From model.fit() to models that survive production — a phased climb through ML fundamentals and the MLOps layer: versioning, serving, drift, scale.

RAG-based AI platform with LLM safety, retrieval, and evaluation pipeline

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.

MLOps

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 MLOps pipeline for house price prediction with multiple models, CI/CD, and API deployment.

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

An end-to-end MLOps pipeline for vehicle insurance data, covering data ingestion, validation, transformation, model training, evaluation, and deployment using MongoDB, AWS, Flask, Docker, and GitHub Actions.

Production-ready MLOps pipeline on AWS using SageMaker, Lambda, CodePipeline, and IaC (Terraform/CDK). Automates training, evaluation, & continuous retraining.

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.

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.

End-to-end MLOps pipeline for news classification — experiment tracking with MLflow, data versioning with DVC, FastAPI serving, drift monitoring with Evidently AI, and a 4-job GitHub Actions CI/CD that builds and pushes to DockerHub on every commit.

Python for MLOps Course

Welcome to this MLOps project, designed to demonstrate a robust pipeline for managing vehicle insurance data. This project aims to showcase skills that go into building and deploying a machine learning pipeline for real-world data management. Follow along to learn about project setup, data processing, model deployment, and CI/CD automation!

This project covers the end to end understanding for creating an ML pipeline and working around it using DVC for experiment tracking and data versioning (using AWS S3)