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  2. Sagemaker will give you the most flexibility but involves more work in setting up and you are charged for endpoints when they are running. Bedrock is focused on offering an API driven and serverless experience. It offers a curated list of foundational models.
    repost.aws/questions/QURQ0DJ5oPSUyyaLv0jjS4…
    🔑 Amazon SageMaker supports the complete machine learning lifecycle. 🔑 Both services offer robust security features but differ in terms of data management and control over the underlying infrastructure. 🔑 Bedrock's setup requires less effort compared to SageMaker, but SageMaker offers more customizability.
    starlane.ai/news/2023-08-amazon-bedrock-vs-ama…
     
  3. People also ask
    Should I use Amazon bedrock or Amazon SageMaker?Both Amazon Bedrock and Amazon SageMaker are powerful tools in the AWS AI/ML service landscape. Choosing between the two depends on your specific needs. If you need to quickly integrate advanced AI capabilities without much customization, Bedrock is the way to go.
    How does Amazon bedrock scalability differ from SageMaker JumpStart?Amazon Bedrock's scalability is influenced by the capabilities of AWS-hosted models, which may offer limited control over resource scaling. SageMaker JumpStart users have granular control over costs through instance type selection and resource allocation, optimizing expenses.
    Does SageMaker JumpStart support Rag models in Amazon bedrock?SageMaker JumpStart provides flexibility for integrating Retrieval-Augmented Generation (RAG) models as per project needs. Integration with RAG models in Amazon Bedrock depends on the availability of such models within the Bedrock ecosystem.
    Does bedrock integrate with other AWS services?Service Integration: Bedrock integrates seamlessly with other AWS services, including SageMaker Experiments (for testing different models) and Pipelines (for managing Foundation Models at scale).
     
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