1. High-Throughput Interfaces for AI Video Analytics


    To build high-throughput interfaces for AI video analytics, you need a cloud infrastructure that can handle both the ingestion of large video data streams and the processing of that data with AI models in real time. For this scenario, I will demonstrate how to create an infrastructure using Azure Video Analyzer, which is designed for real-time video processing and analytics.

    In the example below, we will provision a LivePipeline, which is a type of resource that allows you to ingest live video streams and apply video analytics in real time. The key components of Azure Video Analyzer that we'll use are the LivePipeline for live video streaming and processing, and the Video object, which represents the videos being ingested and analyzed.

    Here's how you might set this up using Pulumi and Azure:

    1. LivePipeline: This resource is used to process live video streams through Azure Video Analyzer. It requires a topology, which defines the structure of the processing pipeline and the AI processing to be done on the stream.

    2. Video: Represents the videos that will be ingested and stored by Azure Video Analyzer. Videos can be analyzed in real time with LivePipelines or processed later with PipelineJob.

    Before you begin, you should have your Azure account set up, and the Pulumi CLI installed and configured with your Azure credentials.

    The following Pulumi Python program defines the necessary resources to create a high-throughput video analytics setup:

    import pulumi from pulumi_azure_native import videoanalyzer # Replace the below variables according to your Azure setup. resource_group_name = 'my-rg' video_analyzer_account_name = 'my-videonalyzer-account' live_pipeline_name = 'my-live-pipeline' video_name = 'my-video' topology_name = 'my-topology' # Provision an Azure Video Analyzer account. video_analyzer_account = videoanalyzer.VideoAnalyzerAccount("videoAnalyzerAccount", resource_group_name=resource_group_name, account_name=video_analyzer_account_name) # Define a live pipeline for real-time video analytics. live_pipeline = videoanalyzer.LivePipeline("livePipeline", resource_group_name=resource_group_name, account_name=video_analyzer_account.name, live_pipeline_name=live_pipeline_name, topology_name=topology_name # Topology name has to be defined and associated in your Azure Video Analyzer Account # The topology defines the structure and processing logic of the live pipeline. ) # Define a video resource for storing and processing video data. video = videoanalyzer.Video("video", resource_group_name=resource_group_name, account_name=video_analyzer_account.name, video_name=video_name, description="A video for analytics", # Additional parameters such as title and mediaInfo can be set as needed. ) # To get the URL that can be used to stream the live video, you would typically use an output export. # For example, this could be a URL with a specific path or some form of access control. pulumi.export("videoUrl", live_pipeline.endpoint) # The 'endpoint' property would refer to the stream endpoint, which may vary based on actual implementation. # Note: The details here like 'topology_name', 'live_pipeline_name', etc. are placeholders # and need to be defined according to the actual resources and names used in your Azure account.

    This program outlines the essential structure required to start working with AI video analytics using Azure Video Analyzer and Pulumi. The LivePipeline and Video resources are foundational building blocks for this purpose.

    For a full-fledged production scenario, you'd need to define the Topology for the LivePipeline that includes the video analytics AI models you want to apply to the live video stream. Additionally, you would need to handle the security, storage, and management of the analyzed video data, which may include integrating with other Azure services such as Azure Storage, Azure Functions, or Azure Event Grid for event-based processing.

    Remember, this setup assumes that you have defined a topology within your Azure Video Analyzer account that matches topology_name variable. A topology is a blueprint that defines the logic of your real-time video processing and is crucial for setting up a LivePipeline.

    Here are some resources for additional reading:

    Ensure you review the documentation for the LivePipeline, Video, and topologies on the Azure website for a thorough understanding of the configurations required for real-time video analytics on Azure.