Feature Flag Management for AI Services with Unleash

Question

Pulumi · Accepted Answer

Feature flags, also known as feature toggles, are a powerful technique for controlling the rollout of features to users in production without deploying new code. They can be used to enable or disable features dynamically, perform A/B testing, and manage operational issues in near real-time.

For managing feature flags within AI services, you might want to use a platform that allows you to toggle flags for services that involve Artificial Intelligence, Machine Learning, or data processing components. Let's create a feature flag using the Pulumi harness provider.

In this program, we will define a `FeatureFlag` resource using Pulumi and Harness. This allows us to create and manage feature flags that can be used within AI services.

Before using the `pulumi_harness` package, you would need to have the Harness provider set up and authenticated. The Pulumi program assumes that this is already done, and your Pulumi environment is configured to use the Harness provider.

Here's what we're going to do:

1. Import the necessary Pulumi libraries.
2. Create a `FeatureFlag` using the `harness.platform.FeatureFlag` resource.
3. Export the feature flag ID for reference.

Let's write a Pulumi program to accomplish this:

```python
import pulumi
import pulumi_harness as harness

# Replace these variables with the appropriate values for your organization/project in Harness
org_id = "your-harness-org-id"
project_id = "your-harness-project-id"

# Defining a new feature flag 
feature_flag = harness.FeatureFlag("ai-feature-flag",
    org_id=org_id,
    project_id=project_id, 
    identifier="ai_new_feature",  # The unique identifier for the feature flag
    name="AI New Feature",  # The name of the feature flag
    kind="boolean",  # The type of feature flag, "boolean" in this case for enable/disable
    permanent=False,  # Indicates if the flag should be permanent
    # Variations are the different values that this feature flag can hold
    variations=[
        harness.FeatureFlagVariationArgs(
            identifier="true",
            value="true",
            name="True",
            description="Feature flag is turned on"
        ),
        harness.FeatureFlagVariationArgs(
            identifier="false",
            value="false",
            name="False",
            description="Feature flag is turned off"
        ),
    ],
    default_on_variation="true",  # Default value when feature flag is turned on
    default_off_variation="false",  # Default value when feature flag is turned off
)

# Export the Feature Flag ID
pulumi.export("feature_flag_id", feature_flag.id)
```

In this program, we first import the necessary Pulumi packages. We then define a new `FeatureFlag` resource, specifying the organization and project identifiers from Harness, a unique identifier for the feature flag itself, along with a user-friendly name. We also define the type of the feature flag as boolean, indicating that this flag can be either turned on or off.

We specify `variations` for the boolean flag, which are the potential values the flag can take. In this case, we have two variations: "true" and "false", with corresponding descriptions.

The `default_on_variation` and `default_off_variation` parameters specify the default values of the feature flag when it is turned on or off, respectively.

Finally, we export the feature flag's `id` so that it can be referenced elsewhere, such as in dashboards, monitoring systems, or other Pulumi stacks.

The use of feature flags can enable more robust, flexible, and controlled approaches to deploying AI services, facilitating testing and gradual rollouts without affecting all users at once. This method helps reduce risk and allows for better management of new features in production environments.