Amazon SageMaker Canvas: Build & Analyze Machine learning Models

Описание: What if you could build a real ML model, no Python, no code and still get production-quality results? That's exactly what we're doing today with ‪@amazonwebservices‬ SageMaker Canvas.

In this video, you'll learn:
✅ How to load a cleaned dataset directly in SageMaker Canvas Build tab
✅ How to configure a Predictive Analysis model targeting median_house_value
✅ How to use the Preview Model feature for quick validation before full training
✅ How to run a Quick Build training job and interpret the results
✅ How to evaluate your model using R², RMSE, and MAE
✅ How to analyze Column Impact / Feature Importance in Canvas
✅ How to read the Actual vs Predicted scatter plot
✅ Real-world business use case: Data-driven house price estimation for real estate

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🔗 USEFUL LINKS
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📄 SageMaker Canvas Documentation:
https://docs.aws.amazon.com/sagemaker...
📄 SageMaker Canvas – Model Evaluation Guide:
https://docs.aws.amazon.com/sagemaker...
📄 AWS SageMaker Canvas Getting Started:
https://docs.aws.amazon.com/sagemaker...

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⏱️ Timestamps Below 👇
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00:00 Introduction
00:17 Project Implementation Flow
01:03 Step 1: Load Cleaned Dataset in Canvas Build Tab
02:10 Step 2: Configure Predictive Analysis Model
07:13 Step 3: Key Insights
09:59 Step 4: Evaluate Results – R², RMSE, and MAE
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