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Accelerate data preparation for ML in Amazon SageMaker Canvas

AWS Machine Learning Blog

You can import data directly through over 50 data connectors such as Amazon Simple Storage Service (Amazon S3), Amazon Athena , Amazon Redshift , Snowflake, and Salesforce. In this walkthrough, we will cover importing your data directly from Snowflake. You can download the dataset loans-part-1.csv csv and loans-part-2.csv.

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Accelerate time to business insights with the Amazon SageMaker Data Wrangler direct connection to Snowflake

AWS Machine Learning Blog

Amazon SageMaker Data Wrangler is a single visual interface that reduces the time required to prepare data and perform feature engineering from weeks to minutes with the ability to select and clean data, create features, and automate data preparation in machine learning (ML) workflows without writing any code.

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Access Snowflake data using OAuth-based authentication in Amazon SageMaker Data Wrangler

Flipboard

Data Wrangler simplifies the data preparation and feature engineering process, reducing the time it takes from weeks to minutes by providing a single visual interface for data scientists to select and clean data, create features, and automate data preparation in ML workflows without writing any code.

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Understanding Everything About UCI Machine Learning Repository!

Pickl AI

Users can download datasets in formats like CSV and ARFF. How to Access and Use Datasets from the UCI Repository The UCI Machine Learning Repository offers easy access to hundreds of datasets, making it an invaluable resource for data scientists, Machine Learning practitioners, and researchers. CSV, ARFF) to begin the download.

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How to Manage Unstructured Data in AI and Machine Learning Projects

DagsHub

Now that you know why it is important to manage unstructured data correctly and what problems it can cause, let's examine a typical project workflow for managing unstructured data. The PartitionerConfig is used to configure how we wish to transform our unstructured data.

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Large Language Models: A Complete Guide

Heartbeat

This step involves several tasks, including data cleaning, feature selection, feature engineering, and data normalization. This process ensures that the dataset is of high quality and suitable for machine learning. The UI can include interactive visualizations or allow users to download the output in different formats.