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Import data from Google Cloud Platform BigQuery for no-code machine learning with Amazon SageMaker Canvas

AWS Machine Learning Blog

This minimizes the complexity and overhead associated with moving data between cloud environments, enabling organizations to access and utilize their disparate data assets for ML projects. You can use SageMaker Canvas to build the initial data preparation routine and generate accurate predictions without writing code.

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Best practices for Meta Llama 3.2 multimodal fine-tuning on Amazon Bedrock

AWS Machine Learning Blog

Prerequisites To use this feature, make sure that you have satisfied the following requirements: An active AWS account. model customization is available in the US West (Oregon) AWS Region. The required training dataset (and optional validation dataset) prepared and stored in Amazon Simple Storage Service (Amazon S3).

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Govern generative AI in the enterprise with Amazon SageMaker Canvas

AWS Machine Learning Blog

This simplifies access to generative artificial intelligence (AI) capabilities to business analysts and data scientists without the need for technical knowledge or having to write code, thereby accelerating productivity. Provide the AWS Region, account, and model IDs appropriate for your environment.

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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.

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Announcing Amazon S3 access point support for Amazon SageMaker Data Wrangler

AWS Machine Learning Blog

We’re excited to announce Amazon SageMaker Data Wrangler support for Amazon S3 Access Points. In this post, we walk you through importing data from, and exporting data to, an S3 access point in SageMaker Data Wrangler. Configure your AWS Identity and Access Management (IAM) role with the necessary policies.

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Fine-tune multimodal models for vision and text use cases on Amazon SageMaker JumpStart

AWS Machine Learning Blog

Prerequisites To try out this solution using SageMaker JumpStart, you need the following prerequisites: An AWS account that will contain all of your AWS resources. An AWS Identity and Access Management (IAM) role to access SageMaker. of persons present’ for the sustainability committee meeting held on 5th April, 2012?

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Build ML features at scale with Amazon SageMaker Feature Store using data from Amazon Redshift

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Amazon Redshift uses SQL to analyze structured and semi-structured data across data warehouses, operational databases, and data lakes, using AWS-designed hardware and ML to deliver the best price-performance at any scale. To do this, we provide an AWS CloudFormation template to create a stack that contains the resources.

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