1. Hosted Inference Endpoints for AI Models on OVH Cloud Project


    Hosting inference endpoints for AI models involves creating a service that can run your machine learning models and expose them through an API for applications to consume. This typically consists of deploying the model onto a cloud provider's infrastructure, configuring an endpoint, and enabling access permissions for clients to make predictions.

    In this case, you want to use OVH Cloud, which isn't directly supported by Pulumi. However, we can work around this by deploying the infrastructure using another cloud provider supported by Pulumi and then connecting it to your OVH Cloud Project if needed.

    Here, I'll show you how to deploy an AI inference endpoint using AWS SageMaker, which is a fully managed service that provides the ability to build, train, and deploy machine learning models. AWS is selected based on the wide range of integrations it offers, and because Pulumi provides good support for it.

    In the following program, we'll deploy an Amazon SageMaker endpoint, which allows you to host your trained machine learning models and perform real-time inference. This SageMaker endpoint could potentially be integrated with your OVH Cloud Project through various means such as using the OVH Cloud network features to direct requests to AWS or using your OVH hosted service to proxy requests to the AWS SageMaker endpoint.

    Here's how you can create a SageMaker endpoint using Pulumi with Python:

    import pulumi import pulumi_aws as aws # Assume that the SageMaker model is already created and we have its name. # Replace 'model_name' with your actual SageMaker model name. sagemaker_model_name = 'model_name' # Create a SageMaker endpoint configuration, specifying the hardware needed. endpoint_config = aws.sagemaker.EndpointConfiguration("aiModelEndpointConfig", production_variants=[ aws.sagemaker.EndpointConfigurationProductionVariantArgs( instance_type="ml.t2.medium", initial_instance_count=1, model_name=sagemaker_model_name, variant_name="AllTraffic", ), ]) # Create the SageMaker endpoint using the configuration. # This endpoint is what applications will call to get predictions from the model. sagemaker_endpoint = aws.sagemaker.Endpoint("aiModelEndpoint", endpoint_config_name=endpoint_config.name, ) # Export the endpoint name so it can be used to invoke the model. pulumi.export('sagemaker_endpoint_name', sagemaker_endpoint.name)

    This Pulumi program sets up a SageMaker endpoint with the following resources:

    1. aws.sagemaker.EndpointConfiguration — This resource is used to define the type and number of instances that will serve the prediction requests. We're using a ml.t2.medium instance type here, but this should be chosen based on the size and computational demands of your machine learning model.

    2. aws.sagemaker.Endpoint — This resource creates the hosted endpoint using the configuration specified above. The endpoint is the entity through which real-time inference requests are processed.

    Once you have this infrastructure in place, you would integrate it with your application. The application would then make HTTP POST requests to the SageMaker Endpoint to receive real-time predictions from your AI model.

    Please note that the actual integration with OVH Cloud would depend on the specifics of your OVH Cloud Project and the architecture you have in mind. If OVH offers a way to create similar inference endpoints directly in their cloud environment and you would prefer to use Python SDK specific to OVH, you would need to consult OVH's documentation or support for the equivalent services and resources.

    Remember to replace 'model_name' with the actual name of your SageMaker model. Also, choose the instance type (ml.t2.medium is just a placeholder) that is appropriate for the computational demands of your AI model.