1. Cache Model Predictions to Reduce Latency in ML Workloads


    To cache model predictions and ultimately reduce latency in machine learning workloads, you need to create a system where predictions are computed once and then stored. When subsequent requests for the same prediction are made, the system should check if the prediction is already cached and serve it directly, thus avoiding the need to recompute the prediction and reducing latency.

    To achieve this in a cloud environment, you can use a combination of managed machine learning services to serve predictions and a caching mechanism. For example, if you are using Google Cloud, you might use Google Cloud's AI Platform Predictions service to serve model predictions, and Memorystore (a fully managed in-memory data store service) to cache these predictions.

    In the illustration below, we will use Google Cloud ML Engine. However, the concept is similar across different cloud providers like Azure or AWS; most have comparable services for machine learning and caching.

    Here's how you might structure such a system with Pulumi using Python:

    1. Model Deployment: First, deploy your pre-trained model to a service like Google Cloud ML Engine which will serve predictions.
    2. Caching Layer: Then, set up a caching layer, such as Google Cloud Memorystore (Redis), where predictions can be stored.
    3. Infrastructure Automation: Use Pulumi's infrastructure as code approach to automate the provisioning of both the prediction service and the caching layer.

    Below is the Pulumi program in Python to set up such an environment:

    import pulumi import pulumi_gcp as gcp # Deploy a pretrained model to Google Cloud ML Engine for serving predictions. ml_engine_model = gcp.ml.EngineModel("mlEngineModel", name="my_model", description="My ML model for predictions", regions=["us-central1"], # Specify the region where the model will be deployed # Online prediction settings. Depending on your needs, logging or other settings can be adjusted. online_prediction_logging=True ) # Set up a Memorystore (Redis) instance to cache predictions. memorystore_instance = gcp.redis.Instance("memorystoreInstance", tier="STANDARD_HA", # High availability tier to ensure redundancy memory_size_gb=1, # The amount of memory allocated to the instance, in GB location_id="us-central1-a", # Location should be near the ML Engine for lower latency authorized_network="default" # The network on which the instance will be available (needs proper setup) ) # Export the URLs/endpoints of the services, which will be useful for integration within applications. pulumi.export("ml_engine_model_id", ml_engine_model.model_id) pulumi.export("memorystore_instance_host", memorystore_instance.host)

    This program does the following:

    1. Defines a pretrained ML model on Google Cloud ML Engine, which serves as the prediction engine. Ensure you have a machine learning model ready for deployment.
    2. Sets up a managed Redis instance on Google Cloud Memorystore, providing an in-memory data structure store, which will be used as the caching layer. Caching is transparent to the machine learning model, so your application needs to implement logic to check the cache before requesting a prediction.
    3. Exports endpoints or identifiers such as the ML Engine model ID and the Memorystore instance host so they can be used in the application that interacts with these services.

    Remember, the actual caching logic will be part of the application you are building; this just sets up the necessary infrastructure. In your application, you would typically:

    • Make a request to the prediction service.
    • If the prediction is not in the cache, compute the prediction and store it in the cache.
    • If it is in the cache, use the cached value to avoid recomputing the prediction.

    When designing your caching layer, consider caching strategies like expiration, invalidation, and cache warming to ensure your cache is effective and your predictions are fresh. It's important to keep in mind that different predictions have different shelf lives, and your cache invalidation strategy should reflect the nature of your model's predictions.