Amazon SageMaker AI Explained & Hands-On | Train and Deploy ML Model Step by Step

🚀 Want to build, train, deploy, and test a Machine Learning model without managing servers? In this video, we understand Amazon SageMaker from scratch — both conceptually and with a complete hands-on demo inside the AWS Console. We start with a simple real-world scenario: predicting whether a student will pass or fail based on hours studied. From there, we walk through the entire end-to-end ML workflow using SageMaker — including training, deployment, testing, troubleshooting, and cleanup. 🕒 Chapters 00:00 – Introduction & Real-World Scenario 00:55 – What Is Amazon SageMaker? 01:18 – High-Level SageMaker Workflow Explained 03:50 – Creating the Training Dataset 05:15 – Uploading Data to Amazon S3 06:50 – Enabling Service Quotas 07:50 – Creating a SageMaker Training Job 09:30 – Understanding Hyperparameters 14:30 – Common Training Job Errors & Fixes 20:00 – Creating a Model 21:05 – Endpoint Configuration Explained and Deploying the Endpoint 23:30 – Testing the Endpoint Using AWS CLI 26:18 – Cleaning Up Resources