A cloud-native iris ML inference service built with FastAPI, containerized using Docker, locally tested on Kubernetes (Minikube), and designed for scalable AWS deployment.

This application demonstrates deploying machine learning models using FastAPI, Docker, and Kubernetes. It includes background task processing with Celery and Redis, and provides endpoints for making predictions and retrieving results. The application uses the Breast Cancer Wisconsin (Diagnostic) dataset for model training and evaluation

Production-ready MLOps demo: real image training, MLflow registry aliases, FastAPI serving, Docker/K8s deploy, drift monitoring & retraining

Practical MLOps template: Argo Workflows pipeline (prep → train → evaluate → register) tracked in MLflow, then deployed as a FastAPI service. Includes Dockerfiles, K8s manifests, HPA, Makefile, and CI. Demo: submit workflow, view MLflow run, call /predict.

NBA player injury prediction system with 78% AUC-ROC. Complete MLOps pipeline: 4.5K+ games processed with 55+ engineered features, FastAPI serving <100ms predictions, Redis caching, MLflow experiment tracking, Prometheus/Grafana monitoring. Production-ready with Docker/K8s deployment, automated testing (90%+ coverage), and real-time API endpoints.

MLOps K8s project with FastAPI model serving

Diabetes Prediction Model – MLOps Project (FastAPI + Docker + K8s)

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Train/log, deploy, and monitor an NLP model like an engineer.

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Foundational MLOps components and onboarding projects — includes MLflow, FEAST, FastAPI backend, Kubernetes, and JupyterHub on K8s.

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End-to-end MLOps pipeline for Cats vs Dogs binary image classification. Kaggle data → CNN → MLflow → FastAPI → Docker → CI/CD → K8s. BITS Pilani Assignment 2 implementation.

Diabetes Prediction MLOps Project (FastAPI + Docker + K8s) – Learn to build and deploy an ML model predicting diabetes based on health metrics (Pregnancies, Glucose, Blood Pressure, BMI, Age) using a Random Forest Classifier on the Pima Indians dataset. Covers model training, FastAPI API, Docker, and Kubernetes deployment.

This project demonstrates how to deploy a machine learning model for image classification using a FastAPI REST API, containerized with Docker, and orchestrated via Kubernetes (K8s). It showcases a full end-to-end MLOps workflow — from model export to scalable deployment — designed to bridge the gap between research and production.