End-to-End MLOps Pipeline for a Computer Vision Retail Analytics Platform

Build a fully automated MLOps pipeline to train, version, package, and deploy a deep learning model that detects and counts customers in retail stores. Implement CI/CD workflows, experiment tracking with MLflow, containerized deployment on Kubernetes, and production monitoring with drift detection and automated retraining triggers.

Feature Store for an Industrial Predictive Maintenance System

Design and implement a centralized feature store to power a predictive maintenance ML model for an industrial manufacturing plant. Incorporate feature engineering pipelines, feature versioning, point-in-time correctness, and feature reuse patterns to accelerate model development and ensure reproducibility across teams.

Scalable Serverless Inference API with Model Monitoring

Create a production-grade, serverless inference API to serve a latency-sensitive ML model for real-time decision-making. Optimize model packaging with Docker, implement blue-green deployment strategies, and build a comprehensive monitoring dashboard that tracks prediction quality, data drift, and system performance metrics.