#[non_exhaustive]
pub struct CreateExperimentInput { pub project: Option<String>, pub name: Option<String>, pub description: Option<String>, pub treatments: Option<Vec<TreatmentConfig>>, pub metric_goals: Option<Vec<MetricGoalConfig>>, pub randomization_salt: Option<String>, pub sampling_rate: Option<i64>, pub online_ab_config: Option<OnlineAbConfig>, pub tags: Option<HashMap<String, String>>, }

Fields (Non-exhaustive)

This struct is marked as non-exhaustive
Non-exhaustive structs could have additional fields added in future. Therefore, non-exhaustive structs cannot be constructed in external crates using the traditional Struct { .. } syntax; cannot be matched against without a wildcard ..; and struct update syntax will not work.
project: Option<String>

The name or ARN of the project that you want to create the new experiment in.

name: Option<String>

A name for the new experiment.

description: Option<String>

An optional description of the experiment.

treatments: Option<Vec<TreatmentConfig>>

An array of structures that describe the configuration of each feature variation used in the experiment.

metric_goals: Option<Vec<MetricGoalConfig>>

An array of structures that defines the metrics used for the experiment, and whether a higher or lower value for each metric is the goal.

randomization_salt: Option<String>

When Evidently assigns a particular user session to an experiment, it must use a randomization ID to determine which variation the user session is served. This randomization ID is a combination of the entity ID and randomizationSalt. If you omit randomizationSalt, Evidently uses the experiment name as the randomizationSalt.

sampling_rate: Option<i64>

The portion of the available audience that you want to allocate to this experiment, in thousandths of a percent. The available audience is the total audience minus the audience that you have allocated to overrides or current launches of this feature.

This is represented in thousandths of a percent. For example, specify 10,000 to allocate 10% of the available audience.

online_ab_config: Option<OnlineAbConfig>

A structure that contains the configuration of which variation to use as the "control" version. tThe "control" version is used for comparison with other variations. This structure also specifies how much experiment traffic is allocated to each variation.

tags: Option<HashMap<String, String>>

Assigns one or more tags (key-value pairs) to the experiment.

Tags can help you organize and categorize your resources. You can also use them to scope user permissions by granting a user permission to access or change only resources with certain tag values.

Tags don't have any semantic meaning to Amazon Web Services and are interpreted strictly as strings of characters.

You can associate as many as 50 tags with an experiment.

For more information, see Tagging Amazon Web Services resources.

Implementations

Consumes the builder and constructs an Operation<CreateExperiment>

Creates a new builder-style object to manufacture CreateExperimentInput

The name or ARN of the project that you want to create the new experiment in.

A name for the new experiment.

An optional description of the experiment.

An array of structures that describe the configuration of each feature variation used in the experiment.

An array of structures that defines the metrics used for the experiment, and whether a higher or lower value for each metric is the goal.

When Evidently assigns a particular user session to an experiment, it must use a randomization ID to determine which variation the user session is served. This randomization ID is a combination of the entity ID and randomizationSalt. If you omit randomizationSalt, Evidently uses the experiment name as the randomizationSalt.

The portion of the available audience that you want to allocate to this experiment, in thousandths of a percent. The available audience is the total audience minus the audience that you have allocated to overrides or current launches of this feature.

This is represented in thousandths of a percent. For example, specify 10,000 to allocate 10% of the available audience.

A structure that contains the configuration of which variation to use as the "control" version. tThe "control" version is used for comparison with other variations. This structure also specifies how much experiment traffic is allocated to each variation.

Assigns one or more tags (key-value pairs) to the experiment.

Tags can help you organize and categorize your resources. You can also use them to scope user permissions by granting a user permission to access or change only resources with certain tag values.

Tags don't have any semantic meaning to Amazon Web Services and are interpreted strictly as strings of characters.

You can associate as many as 50 tags with an experiment.

For more information, see Tagging Amazon Web Services resources.

Trait Implementations

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