Deploy Once, Predict for All: The Power of Multi-Model AI

🚀 Discover the Future of Real-Time Forecasting with Amazon SageMaker!
In this video, we walk you through how to deploy a multi-model endpoint in SageMaker to serve multiple trained machine learning models from a single real-time inference endpoint.

📈 Watch how a business analyst reacts to AI-driven predictions for housing prices across multiple cities — powered by SageMaker’s real-time inference!

💡 What You’ll Learn:

How SageMaker supports real-time, serverless, and batch inference

Step-by-step deployment of multiple XGBoost models using a notebook instance

Real-world use case: house price prediction across different locations

Cost-saving benefits of multi-model endpoints

Hands-on demo from notebook to deployment to inference

🏠 Dataset: Synthetic housing prices (bedrooms, square feet, bathrooms)
🧠 Models: Binary classification XGBoost models for city-specific price forecasts
🔍 Role: You act as a machine learning engineer deploying models for analysts

👉 Whether you're in data science, real estate, or cloud ML — this tutorial gives you practical skills with real impact.

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