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Promote pipelines in a multi-environment setup using Amazon SageMaker Model Registry, HashiCorp Terraform, GitHub, and Jenkins CI/CD

AWS Machine Learning Blog

Policy 3 – Attach AWSLambda_FullAccess , which is an AWS managed policy that grants full access to Lambda, Lambda console features, and other related AWS services.

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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. In this post, we walk you through the process of scaling your ML workloads using SageMaker Studio and SageMaker HyperPod.

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Four approaches to manage Python packages in Amazon SageMaker Studio notebooks

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Because of this difference, there are some specifics of how you create and manage virtual environments in Studio notebooks , for example usage of Conda environments or persistence of ML development environments between kernel restarts. Refer to SageMaker Studio Lifecycle Configuration Samples for more samples and use cases.

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Dive deep into vector data stores using Amazon Bedrock Knowledge Bases

AWS Machine Learning Blog

Generative AI solutions often use Retrieval Augmented Generation (RAG) architectures, which augment external knowledge sources for improving content quality, context understanding, creativity, domain-adaptability, personalization, transparency, and explainability.

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