Evidently
import { Evidently } from "https://aws-api.deno.dev/v0.3/services/evidently.ts?docs=full";§Constructors
§Methods
This operation assigns feature variation to user sessions.
For each user session, you pass in an entityID that represents the user.
Evidently then checks the evaluation rules and assigns the variation.
The first rules that are evaluated are the override rules.
If the user's entityID matches an override rule, the user is served the variation specified by that rule.
Next, if there is a launch of the feature, the user might be assigned to a variation in the launch. The chance of this depends on the percentage of users that are allocated to that launch. If the user is enrolled in the launch, the variation they are served depends on the allocation of the various feature variations used for the launch.
If the user is not assigned to a launch, and there is an ongoing experiment for this feature, the user might be assigned to a variation in the experiment. The chance of this depends on the percentage of users that are allocated to that experiment. If the user is enrolled in the experiment, the variation they are served depends on the allocation of the various feature variations used for the experiment.
If the user is not assigned to a launch or experiment, they are served the default variation.
Creates an Evidently experiment. Before you create an experiment, you must create the feature to use for the experiment.
An experiment helps you make feature design decisions based on evidence and data. An experiment can test as many as five variations at once. Evidently collects experiment data and analyzes it by statistical methods, and provides clear recommendations about which variations perform better.
Don't use this operation to update an existing experiment. Instead, use UpdateExperiment.
Creates an Evidently feature that you want to launch or test. You can define up to five variations of a feature, and use these variations in your launches and experiments. A feature must be created in a project. For information about creating a project, see CreateProject.
Don't use this operation to update an existing feature. Instead, use UpdateFeature.
Creates a launch of a given feature. Before you create a launch, you must create the feature to use for the launch.
You can use a launch to safely validate new features by serving them to a specified percentage of your users while you roll out the feature. You can monitor the performance of the new feature to help you decide when to ramp up traffic to more users. This helps you reduce risk and identify unintended consequences before you fully launch the feature.
Don't use this operation to update an existing launch. Instead, use UpdateLaunch.
Creates a project, which is the logical object in Evidently that can contain features, launches, and experiments. Use projects to group similar features together.
To update an existing project, use UpdateProject.
Deletes an Evidently experiment. The feature used for the experiment is not deleted.
To stop an experiment without deleting it, use StopExperiment.
Deletes an Evidently launch. The feature used for the launch is not deleted.
To stop a launch without deleting it, use StopLaunch.
Deletes an Evidently project. Before you can delete a project, you must delete all the features that the project contains. To delete a feature, use DeleteFeature.
This operation assigns a feature variation to one given user session.
You pass in an entityID that represents the user.
Evidently then checks the evaluation rules and assigns the variation.
The first rules that are evaluated are the override rules.
If the user's entityID matches an override rule, the user is served the variation specified by that rule.
Next, if there is a launch of the feature, the user might be assigned to a variation in the launch. The chance of this depends on the percentage of users that are allocated to that launch. If the user is enrolled in the launch, the variation they are served depends on the allocation of the various feature variations used for the launch.
If the user is not assigned to a launch, and there is an ongoing experiment for this feature, the user might be assigned to a variation in the experiment. The chance of this depends on the percentage of users that are allocated to that experiment. If the user is enrolled in the experiment, the variation they are served depends on the allocation of the various feature variations used for the experiment.
If the user is not assigned to a launch or experiment, they are served the default variation.
Returns the details about one experiment. You must already know the experiment name. To retrieve a list of experiments in your account, use ListExperiments.
Retrieves the results of a running or completed experiment.
Returns the details about one feature. You must already know the feature name. To retrieve a list of features in your account, use ListFeatures.
Returns the details about one launch. You must already know the launch name. To retrieve a list of launches in your account, use ListLaunches.
Returns the details about one launch. You must already know the project name. To retrieve a list of projects in your account, use ListProjects.
Returns configuration details about all the experiments in the specified project.
Returns configuration details about all the features in the specified project.
Returns configuration details about all the launches in the specified project.
Returns configuration details about all the projects in the current Region in your account.
Displays the tags associated with an Evidently resource.
Sends performance events to Evidently. These events can be used to evaluate a launch or an experiment.
Starts an existing experiment. To create an experiment, use CreateExperiment.
Starts an existing launch. To create a launch, use CreateLaunch.
Stops an experiment that is currently running. If you stop an experiment, you can't resume it or restart it.
Stops a launch that is currently running. After you stop a launch, you will not be able to resume it or restart it. Also, it will not be evaluated as a rule for traffic allocation, and the traffic that was allocated to the launch will instead be available to the feature's experiment, if there is one. Otherwise, all traffic will be served the default variation after the launch is stopped.
Assigns one or more tags (key-value pairs) to the specified CloudWatch Evidently resource. Projects, features, launches, and experiments can be tagged.
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 use the TagResource action with a resource that already has tags.
If you specify a new tag key for the resource, this tag is appended to the list of tags associated with the alarm.
If you specify a tag key that is already associated with the resource, the new tag value that you specify replaces the previous value for that tag.
You can associate as many as 50 tags with a resource.
For more information, see Tagging Amazon Web Services resources.
Updates an Evidently experiment.
Don't use this operation to update an experiment's tag. Instead, use TagResource.
Updates an existing feature.
You can't use this operation to update the tags of an existing feature. Instead, use TagResource.
Updates a launch of a given feature.
Don't use this operation to update the tags of an existing launch. Instead, use TagResource.
Updates the description of an existing project.
To create a new project, use CreateProject.
Don't use this operation to update the data storage options of a project. Instead, use UpdateProjectDataDelivery.
Don't use this operation to update the tags of a project. Instead, use TagResource.
Updates the data storage options for this project. If you store evaluation events, you an keep them and analyze them on your own. If you choose not to store evaluation events, Evidently deletes them after using them to produce metrics and other experiment results that you can view.
You can't specify both cloudWatchLogs and s3Destination in the same operation.