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Accelerate data preparation for ML in Amazon SageMaker Canvas

AWS Machine Learning Blog

Data preparation is a crucial step in any machine learning (ML) workflow, yet it often involves tedious and time-consuming tasks. Amazon SageMaker Canvas now supports comprehensive data preparation capabilities powered by Amazon SageMaker Data Wrangler.

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Emerging Data Science Trends in 2025 You Need to Know

Pickl AI

The Rise of Augmented Analytics Augmented analytics is revolutionizing how data insights are generated by integrating artificial intelligence (AI) and machine learning (ML) into analytics workflows. What is the Data Science Trend in 2025?

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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

Amazon DataZone makes it straightforward for engineers, data scientists, product managers, analysts, and business users to access data throughout an organization so they can discover, use, and collaborate to derive data-driven insights.

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

Flipboard

Snowflake is an AWS Partner with multiple AWS accreditations, including AWS competencies in machine learning (ML), retail, and data and analytics. Data scientist experience In this section, we cover how data scientists can connect to Snowflake as a data source in Data Wrangler and prepare data for ML.

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Accelerate time to insight with Amazon SageMaker Data Wrangler and the power of Apache Hive

AWS Machine Learning Blog

Amazon SageMaker Data Wrangler reduces the time it takes to aggregate and prepare data for machine learning (ML) from weeks to minutes in Amazon SageMaker Studio. Starting today, you can connect to Amazon EMR Hive as a big data query engine to bring in large datasets for ML.

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Simplify data prep for generative AI with Amazon SageMaker Data Wrangler

AWS Machine Learning Blog

Data preparation is important at multiple stages in Retrieval Augmented Generation ( RAG ) models. Specifically, we clean the data and create RAG artifacts to answer the questions about the content of the dataset. Choose Create on the right side of page, then give a data flow name and select Create. Choose your domain.

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Use LangChain with PySpark to process documents at massive scale with Amazon SageMaker Studio and Amazon EMR Serverless

AWS Machine Learning Blog

With the introduction of EMR Serverless support for Apache Livy endpoints , SageMaker Studio users can now seamlessly integrate their Jupyter notebooks running sparkmagic kernels with the powerful data processing capabilities of EMR Serverless. This same interface is also used for provisioning EMR clusters. python3.11-pip jars/livy-repl_2.12-0.7.1-incubating.jar

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