Remove 2012 Remove Clustering Remove Data Scientist
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Scale ML workflows with Amazon SageMaker Studio and Amazon SageMaker HyperPod

AWS Machine Learning Blog

This integration addresses these hurdles by providing data scientists and ML engineers with a comprehensive environment that supports the entire ML lifecycle, from development to deployment at scale. This eliminates the need for data migration or code changes as you scale.

ML 101
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Implement user-level access control for multi-tenant ML platforms on Amazon SageMaker AI

AWS Machine Learning Blog

For instance, you might want to make sure data scientists can’t accidentally delete another team member’s endpoints or access SageMaker training jobs they don’t own. The following policy restricts SageMaker Studio users access to EMR clusters by requiring that the cluster be tagged with a user key matching the user’s SourceIdentity.

ML 61
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Fine-tune multimodal models for vision and text use cases on Amazon SageMaker JumpStart

AWS Machine Learning Blog

Data scientists and developers can quickly prototype and experiment with various ML use cases, accelerating the development and deployment of ML applications. of persons present’ for the sustainability committee meeting held on 5th April, 2012? WASHINGTON, D. 20036 1128 SIXTEENTH ST., WASHINGTON, D. 20036 What is the ‘no.

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Transforming financial analysis with CreditAI on Amazon Bedrock: Octus’s journey with AWS

AWS Machine Learning Blog

Cost-efficiency and infrastructure optimization By moving away from GPU-based clusters to Fargate, our monthly infrastructure costs are now 78.47% lower, and our per-question costs have reduced by 87.6%. With a decade of experience at Amazon, having joined in 2012, Kshitiz has gained deep insights into the cloud computing landscape.

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Evaluating generative AI models with Amazon Nova LLM-as-a-Judge on Amazon SageMaker AI

AWS Machine Learning Blog

Assign as trust relationship to your IAM role the following policy: { "Version": "2012-10-17", "Statement": [ { "Sid": "", "Effect": "Allow", "Principal": { "Service": [ "bedrock.amazonaws.com", "sagemaker.amazonaws.com" ] }, "Action": "sts:AssumeRole" } ] } Clone the GitHub repository with the assets for this deployment.

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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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Multi-account support for Amazon SageMaker HyperPod task governance

AWS Machine Learning Blog

Organizations building or adopting generative AI use GPUs to run simulations, run inference (both for internal or external usage), build agentic workloads, and run data scientists’ experiments. The workloads range from ephemeral single-GPU experiments run by scientists to long multi-node continuous pre-training runs.