Centralized Feature Flag Management for AI Experimentation

Question

Pulumi · Accepted Answer

The concept of feature flags is a powerful technique for managing and controlling the rollout of new features in your applications. It allows developers to turn features on and off without deploying new code, which helps in testing and gradual rollout of features to users based on certain conditions. For AI experimentation, feature flags can enable you to dynamically control the behavior of AI models or experiments to quickly assess the performance while reducing risks.

Pulumi provides support to manage feature flags through various cloud providers. For this example, I'll show you how to create a centralized feature flag management system using Harness' feature flags capabilities, as indicated in the Pulumi registry results for `harness.platform.FeatureFlag`.

To start, we'll create a basic Pulumi program to:

1. Generate a Harness Feature Flag, which acts as a toggle for enabling or disabling features in your AI application.
2. Generate an API key, which will be necessary when integrating the feature flag service with your application.

Let's go through the Pulumi program written in Python:

```python
import pulumi
import pulumi_harness as harness

# Replace these with your actual values
org_id = "your-org-id"
project_id = "your-project-id"
environment_id = "your-env-id"

# 1. Creating a feature flag in Harness.
feature_flag = harness.FeatureFlag("ai-experiment-feature-flag",
    orgId=org_id,
    projectId=project_id,
    identifier="ai-experiment-flag",
    permanent=False,
    name="AI Experiment",
    variations=[
        {
            "identifier": "on",
            "name": "On Variation",
            "description": "The feature is on",
            "value": "true", # Using strings for boolean for simplicity in value handling
        }, 
        {
            "identifier": "off",
            "name": "Off Variation",
            "description": "The feature is off",
            "value": "false",
        }
    ],
    defaultOnVariation="on",
    defaultOffVariation="off"
)

# 2. Generating a feature flag API key for authenticating requests from your application to the Harness feature flag service.
feature_flag_api_key = harness.FeatureFlagApiKey("ai-experiment-feature-flag-key",
    envId=environment_id,
    orgId=org_id,
    projectId=project_id,
    identifier="ai-experiment-flag-api-key",
    name="AI Experiment API Key",
    type="PRIMARY"
)

# Exporting the Feature Flag ID and API Key value for usage in your application code or CI/CD pipelines.
pulumi.export("feature_flag_id", feature_flag.id)
pulumi.export("feature_flag_api_key", feature_flag_api_key.value)
```

### Explanation

In the program above, we start by importing the necessary Pulumi modules for Python, including the `pulumi_harness` which allows us to manage Harness resources.

We define the organization ID, project ID, and environment ID that are required by Harness to organize and manage the resources properly. Make sure to replace the placeholders with the actual values.

Then we create a `FeatureFlag` resource, which in this case, is named `ai-experiment-feature-flag`. We provide it with a name, an identifier, a flag to denote it's not permanent (`permanent=False`), and an array of variations. Variations are the possible values the feature flag can take; in this case, it's simply "on" or "off". We also specify what the default variation should be when the flag is turned "on" and when it's "off".

Next, we create an API key associated with the environment we want to control with the `FeatureFlagApiKey` resource. This key is what our application will use to authenticate against the Harness feature management service and toggle the feature flags as needed.

Finally, we export the feature flag ID and API key value. These values can be used in your application code to interact with the feature flag.

Remember, the process requires an actual Harness account, and the IDs for the organization, project, and environment must be obtained from there. The `identifier` fields are values that you decide and should align with identifiers used in your application code.

This program provides a simple template for setting up feature flags for AI experimentation. When integrated into your application logic, you can begin to trial features gated by these flags.