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Integrate HyperPod clusters with Active Directory for seamless multi-user login

AWS Machine Learning Blog

Amazon SageMaker HyperPod is purpose-built to accelerate foundation model (FM) training, removing the undifferentiated heavy lifting involved in managing and optimizing a large training compute cluster. In this solution, HyperPod cluster instances use the LDAPS protocol to connect to the AWS Managed Microsoft AD via an NLB.

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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

Cost optimization – The serverless nature of the integration means you only pay for the compute resources you use, rather than having to provision and maintain a persistent cluster. SageMaker Studio runs inside an AWS managed virtual private cloud ( VPC ), with network access for SageMaker Studio domains, in this setup configured as VPC-only.

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Bring legacy machine learning code into Amazon SageMaker using AWS Step Functions

AWS Machine Learning Blog

Tens of thousands of AWS customers use AWS machine learning (ML) services to accelerate their ML development with fully managed infrastructure and tools. The data scientist is responsible for moving the code into SageMaker, either manually or by cloning it from a code repository such as AWS CodeCommit.

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Scale ML workflows with Amazon SageMaker Studio and Amazon SageMaker HyperPod

AWS Machine Learning Blog

Solution overview Implementing the solution consists of the following high-level steps: Set up your environment and the permissions to access Amazon HyperPod clusters in SageMaker Studio. You can now use SageMaker Studio to discover the SageMaker HyperPod clusters, and view cluster details and metrics.

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Build a reverse image search engine with Amazon Titan Multimodal Embeddings in Amazon Bedrock and AWS managed services

AWS Machine Learning Blog

Prerequisites To implement the proposed solution, make sure that you have the following: An AWS account and a working knowledge of FMs, Amazon Bedrock , Amazon SageMaker , Amazon OpenSearch Service , Amazon S3 , and AWS Identity and Access Management (IAM). Amazon Titan Multimodal Embeddings model access in Amazon Bedrock.

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Configure cross-account access of Amazon Redshift clusters in Amazon SageMaker Studio using VPC peering

AWS Machine Learning Blog

As described in the AWS Well-Architected Framework , separating workloads across accounts enables your organization to set common guardrails while isolating environments. Organizations with a multi-account architecture typically have Amazon Redshift and SageMaker Studio in two separate AWS accounts. Select VPC Only , then choose Next.

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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. Prerequisites To continue with the examples in this post, you need to create the required AWS resources.

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