Train an XGBoost Model on SageMaker
Train an XGBoost classification model using SageMaker's built-in algorithm. Configure hyperparameters, specify training channels, and understand the managed training lifecycle.
AWS Services You'll Use
Lab Details
- Track
- MLA-C01
- Learning Path
- SageMaker Summit
- Difficulty
- Advanced
- Duration
- 40 min
- Environment
- Real AWS Sandbox
Why This Lab?
Unlike video courses or multiple-choice quizzes, this lab drops you into a real AWS sandbox where you build, deploy, and validate working infrastructure. Our automated validators check your actual AWS resources — not honor system, real proof. Complete it and it shows up in your verified portfolio with a timestamp and badge.
More from SageMaker Summit
Set Up SageMaker Studio & Notebooks
Configure SageMaker Studio IDE and run your first ML notebook.
Advanced · 30 min🚀Deploy a Real-Time Inference Endpoint
Deploy your trained model to a SageMaker real-time endpoint.
Advanced · 35 min📦Run Batch Inference with SageMaker
Process large datasets offline with SageMaker Batch Transform.
Advanced · 30 minReady to build this for real?
Get a safe AWS sandbox, step-by-step guidance, and automated validation. No risk of surprise charges. Your completed lab shows up in your verified portfolio.
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