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Unlock the power of data governance and no-code machine learning with Amazon SageMaker Canvas and Amazon DataZone

AWS Machine Learning Blog

Choose Data Wrangler in the navigation pane. On the Import and prepare dropdown menu, choose Tabular. You can review the generated Data Quality and Insights Report to gain a deeper understanding of the data, including statistics, duplicates, anomalies, missing values, outliers, target leakage, data imbalance, and more.

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Reimagining Data Preparation for High-Impact Decision-Making

The Data Administration Newsletter

Data often arrives from multiple sources in inconsistent forms, including duplicate entries from CRM systems, incomplete spreadsheet records, and mismatched naming conventions across databases. Data […] These issues slow analysis pipelines and demand time-consuming cleanup.

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Integrating custom dependencies in Amazon SageMaker Canvas workflows

AWS Machine Learning Blog

Amazon SageMaker Canvas is a low-code no-code (LCNC) ML platform that guides users through every stage of the ML journey, from initial data preparation to final model deployment. Without writing a single line of code, users can explore datasets, transform data, build models, and generate predictions.

Python 100