No-Code ML: Data Prep and Model Building with SageMaker Canvas
Key takeaways
- SageMaker Canvas enables no-code connection to Snowflake for ML.
- Data Wrangler simplifies visual data preparation and transformation.
- XGBoost models can be trained without writing code for tasks like fraud detection.
- This process accelerates the development of predictive analytics solutions.
Who benefits
Summary
This second part of the no-code ML series demonstrates connecting Amazon SageMaker Canvas to Snowflake, preparing transaction data using Data Wrangler, and training an XGBoost fraud detection model. All these steps are performed without writing machine learning code, setting the stage for visualization in Part 3.
Why it matters
This part shows how to practically apply no-code tools to transform raw data into a trained predictive model, enabling rapid prototyping and deployment of ML solutions.
How to implement this in your domain
- 1Connect Amazon SageMaker Canvas to your pre-configured Snowflake environment.
- 2Utilize Data Wrangler within SageMaker Canvas for visual data preparation and transformation.
- 3Join necessary transaction data for your specific use case, such as fraud detection.
- 4Train an XGBoost model using the prepared data within SageMaker Canvas.
- 5Evaluate the trained model's performance before proceeding to visualization.
Original post by Anu Kaggadasapura Nagaraja
"In Part 2 of this no-code ML series, you connect Amazon SageMaker Canvas to Snowflake, prepare and join transaction data with Data Wrangler visual transformations, and train an XGBoost fraud detection model. All without writing machine learning code, laying the groundwork for int…"
View on XOriginally posted by Anu Kaggadasapura Nagaraja on X · view source
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