90% ML Models Fail in Production — Build a Real MLOps Pipeline on AWS (End-to-End)

Description

💡 Description 🚨 90% of Machine Learning models never reach production. Not because the models are bad — but because there is no real MLOps pipeline. In this video, I show how I built a production-grade AI system from scratch using real healthcare data (55,000+ records). From raw CSV files → to a fully deployed system on AWS Kubernetes (EKS). 🔥 What you'll learn How to go from raw data to production-ready ML system Why notebook-based ML projects fail in real-world Data quality & labeling challenges in production Building end-to-end ML pipelines using scikit-learn Experiment tracking with MLflow Reproducibility using DVC FastAPI for model serving Input validation using Pydantic Drift monitoring using PSI Dockerizing ML systems Deploying to AWS EKS (Kubernetes) CI/CD using GitHub Actions 🚀 Full Course (Limited Offer) 👉 https://www.udemy.com/course/ai-system-design-mlops-from-raw-data-to-aws-kubernetes/?couponCode=CF1E8032C0AB712C3D2B 👨‍💻 About Me Principal Architect | 7x Microsoft MVP | IIT Madras AI/ML 48K+ Students across courses 👉 https://rahulsahay.com #MLOps #MachineLearning #AIEngineering #AWS #Kubernetes #MLflow #FastAPI #DataScience #AIProjects #ProductionML