Use to specify a automatic model evaluation job. The EvaluationDatasetMetricConfig object is used to specify the prompt datasets, task type, and metric names.

A JSON array that provides the status of the model evaluation jobs being deleted.

An array of model evaluation jobs to be deleted, and their associated statuses.

CloudWatch logging configuration.

Summary information for a custom model.

Contains the ARN of the Amazon Bedrock models specified in your model evaluation job. Each Amazon Bedrock model supports different inferenceParams. To learn more about supported inference parameters for Amazon Bedrock models, see Inference parameters for foundation models.

Used to specify either a AutomatedEvaluationConfig or HumanEvaluationConfig object.

Used to specify the name of a built-in prompt dataset and optionally, the Amazon S3 bucket where a custom prompt dataset is saved.

The location in Amazon S3 where your prompt dataset is stored.

Defines the built-in prompt datasets, built-in metric names and custom metric names, and the task type.

Used to define the models you want used in your model evaluation job. Automated model evaluation jobs support only a single model. In a human-based model evaluation job, your annotator can compare the responses for up to two different models.

Defines the models used in the model evaluation job.

The Amazon S3 location where the results of your model evaluation job are saved.

A summary of the model evaluation job.

Information about a foundation model.

Details about whether a model version is available or deprecated.

Summary information for a foundation model.

Contains filter strengths for harmful content. Guardrails support the following content filters to detect and filter harmful user inputs and FM-generated outputs.

Contains filter strengths for harmful content. Guardrails support the following content filters to detect and filter harmful user inputs and FM-generated outputs.

Contains details about how to handle harmful content.

Contains details about how to handle harmful content.

The details for the guardrails contextual grounding filter.

The filter configuration details for the guardrails contextual grounding filter.

The details for the guardrails contextual grounding policy.

The policy configuration details for the guardrails contextual grounding policy.

The managed word list that was configured for the guardrail. (This is a list of words that are pre-defined and managed by guardrails only.)

The managed word list to configure for the guardrail.

The PII entity configured for the guardrail.

The PII entity to configure for the guardrail.

The regular expression configured for the guardrail.

The regular expression to configure for the guardrail.

Contains details about PII entities and regular expressions configured for the guardrail.

Contains details about PII entities and regular expressions to configure for the guardrail.

Contains details about a guardrail.

Details about topics for the guardrail to identify and deny.

Details about topics for the guardrail to identify and deny.

Contains details about topics that the guardrail should identify and deny.

Contains details about topics that the guardrail should identify and deny.

A word configured for the guardrail.

A word to configure for the guardrail.

Contains details about the word policy configured for the guardrail.

Contains details about the word policy to configured for the guardrail.

Specifies the custom metrics, how tasks will be rated, the flow definition ARN, and your custom prompt datasets. Model evaluation jobs use human workers only support the use of custom prompt datasets. To learn more about custom prompt datasets and the required format, see Custom prompt datasets.

In a model evaluation job that uses human workers you must define the name of the metric, and how you want that metric rated ratingMethod, and an optional description of the metric.

Contains SageMakerFlowDefinition object. The object is used to specify the prompt dataset, task type, rating method and metric names.

Information about tne imported model.

Contains information about a model.

Contains information about an inference profile.

Configuration fields for invocation logging.

Contains details about each model copy job.

Information about one customization job

Data source for the imported model.

Information about the import job.

Details about the location of the input to the batch inference job.

Contains the configuration of the S3 location of the output data.

Contains the configuration of the S3 location of the output data.

Contains the configuration of the S3 location of the output data.

A summary of a batch inference job.

S3 Location of the output data.

A summary of information about a Provisioned Throughput.

S3 configuration for storing log data.

The Amazon S3 data source of the imported job.

Definition of the key/value pair for a tag.

S3 Location of the training data.

Metrics associated with the custom job.

Array of up to 10 validators.

Information about a validator.

The metric for the validator.

VPC configuration.