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Results 31 - 40 of 446 for host:docs.aws.amazon.com (0.23 sec)

  1. Connect to your Linux instance from Windows usi...

    Connect to your Linux instances from Windows using PuTTY, a free SSH client for Windows.
    docs.aws.amazon.com/AWSEC2/latest/UserGuide/put...
    Registered: 2022-07-01 01:37
    - Last Modified: 2022-06-30 20:53
    - 26K bytes
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  2. Set up dynamic DNS on Your Amazon Linux instanc...

    Use a dynamic DNS provider with Amazon EC2 and configure the instance to update the IP address associated with a public DNS name each time the instance starts.
    docs.aws.amazon.com/AWSEC2/latest/UserGuide/dyn...
    Registered: 2022-07-01 01:36
    - Last Modified: 2022-06-30 20:54
    - 15K bytes
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  3. View Model Groups and Versions - Amazon SageMaker

    Model groups and versions help you organize your models. You can view a list of the model versions in a model group.
    docs.aws.amazon.com/sagemaker/latest/dg/model-r...
    Registered: 2022-07-01 01:46
    - Last Modified: 2022-06-30 20:13
    - 10.8K bytes
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  4. View the Details of a Model Version - Amazon Sa...

    View details of a specific model version by using either the AWS SDK for Python (Boto3) or by using Amazon SageMaker Studio.
    docs.aws.amazon.com/sagemaker/latest/dg/model-r...
    Registered: 2022-07-01 01:47
    - Last Modified: 2022-06-30 20:13
    - 15.7K bytes
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  5. Deploy a Model from the Registry - Amazon SageM...

    Deploy a registered model to a SageMaker endpoint for real-time inference.
    docs.aws.amazon.com/sagemaker/latest/dg/model-r...
    Registered: 2022-07-01 01:47
    - Last Modified: 2022-06-30 20:13
    - 20.2K bytes
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  6. Detect Pretraining Data Bias - Amazon SageMaker

    Algorithmic bias, discrimination, fairness, and related topics have been studied across disciplines such as law, policy, and computer science. A computer system might be considered biased if it discriminates against certain individuals or groups of individuals. The machine learning models powering these applications learn from data and this data could reflect disparities or other inherent biases. For example, the training data may not have sufficient representation of various demographic groups or may contain biased labels. The machine learning models trained on datasets that exhibit these biases could end up learning them and then reproduce or even exacerbate those biases in their predictions. The field of machine learning provides an opportunity to address biases by detecting them and measuring them at each stage of the ML lifecycle. You can use Amazon SageMaker Clarify to determine whether data used for training models encodes any bias
    docs.aws.amazon.com/sagemaker/latest/dg/clarify...
    Registered: 2022-07-01 01:47
    - Last Modified: 2022-06-30 20:01
    - 16.5K bytes
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  7. Amazon SageMaker Clarify Model Explainability -...

    Amazon SageMaker Clarify provides tools to explain how machine learning (ML) models make predictions.
    docs.aws.amazon.com/sagemaker/latest/dg/clarify...
    Registered: 2022-07-01 01:47
    - Last Modified: 2022-06-30 20:10
    - 14.6K bytes
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  8. Shut down and Update SageMaker Studio - Amazon ...

    To update Amazon SageMaker Studio to the latest release, you must shut down the JupyterServer app. You can shut down the JupyterServer app from the SageMaker console or from within Studio. After the JupyterServer app is shut down, you must reopen Studio through the SageMaker console which creates a new version of the JupyterServer app.
    docs.aws.amazon.com/sagemaker/latest/dg/studio-...
    Registered: 2022-07-01 01:46
    - Last Modified: 2022-06-30 19:53
    - 10.8K bytes
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  9. Shut down and Update Studio Apps - Amazon SageM...

    To update an Amazon SageMaker Studio app to the latest release, you must first shut down the corresponding KernelGateway app from the SageMaker console. After the KernelGateway app is shut down, you must reopen it through SageMaker Studio by running a new kernel. The kernel automatically updates. Any unsaved notebook information is lost in the process. The user data in the Amazon EFS volume isn't impacted.
    docs.aws.amazon.com/sagemaker/latest/dg/studio-...
    Registered: 2022-07-01 01:46
    - Last Modified: 2022-06-30 19:53
    - 9.4K bytes
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  10. Delete a Model Group - Amazon SageMaker

    You can delete an Amazon SageMaker model group in your account in Studio.
    docs.aws.amazon.com/sagemaker/latest/dg/model-r...
    Registered: 2022-07-01 01:46
    - Last Modified: 2022-06-30 20:13
    - 9.8K bytes
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