Video Intelligence API for Content Moderation

Question

Pulumi · Accepted Answer

Content moderation is a crucial aspect of building a media application that ensures the content being uploaded or streamed does not contain any inappropriate material. Using a cloud provider's video intelligence or media services API, you can automate the process of content moderation to identify and potentially flag or take action on content that violates your guidelines.

The Pulumi ecosystem allows you to define and manage cloud resources using code in your favorite programming languages like Python. Using Pulumi, we can configure and deploy services that provide video intelligence capabilities for content moderation.

For example, if we were to use AWS, we could utilize the Amazon Chime SDK media pipeline alongside AWS services like Amazon Rekognition for video analysis. Similarly, for Azure, we could leverage Azure Video Analyzer (formerly known as Azure Media Services Video Indexer) which provides similar capabilities.

In this guide, I will walk you through setting up Azure's Video Analyzer using Pulumi in Python. Azure Video Analyzer integrates various Azure services to enable content moderation, object detection, and other advanced video processing features.

Here's how we can provision such services with Pulumi:

```python
import pulumi
import pulumi_azure_native as azure_native

# Create a new resource group for your Video Analyzer resources
resource_group = azure_native.resources.ResourceGroup("videoanalyzer-rg")

# Provision an Azure Video Analyzer account within the resource group
video_analyzer_account = azure_native.videoanalyzer.Account(
    "videoanalyzeraccount",
    resource_group_name=resource_group.name,
    location=resource_group.location,
    # The account requires specifying various properties like encryption, managed identities, etc.
    # Adjust these configurations to your requirements. This example shows basic configuration.
    identity=azure_native.videoanalyzer.IdentityArgs(
        type="SystemAssigned",
    )
)

# Output the primary endpoint of the Video Analyzer account
pulumi.export("video_analyzer_primary_endpoint", video_analyzer_account.primary_endpoint)
```

In this code:

- We create a new Azure resource group, which is a container that holds related resources for an Azure solution. The `ResourceGroup` is where all resources in this example will live.
- Next, we define an Azure Video Analyzer account, representing the main service for analyzing video content. Here I've chosen a system-assigned managed identity for simplicity, but you might want to use a user-assigned identity for more complex scenarios.
- Finally, we export the primary endpoint of the Video Analyzer account to easily access it after deployment.

The Azure Video Analyzer account is a complex service with many configurable properties, such as encryption settings, associated storage accounts, and network configurations. This basic example provides a starting point, but you should adjust the configuration to meet your specific needs concerning security, scale, and compliance. You can refer to the [Azure Video Analyzer documentation](https://www.pulumi.com/registry/packages/azure-native/api-docs/videoanalyzer/) on Pulumi for more information about each configurable property.

Running this Pulumi program with `pulumi up` will provision these resources in Azure. After provisioning, your Azure Video Analyzer instance will be ready to start processing video content and use intelligent video analytics and content moderation capabilities provided by Azure.