1. Machine Learning Feature Stores Using AWS Neptune


    Feature stores are a relatively new architectural component in a machine learning infrastructure, designed to serve as a single source of truth for both the features used in training models and those used in making predictions in production systems. AWS Neptune is a fully managed graph database service that can be used for storing and managing relationships between your data points, but it isn't specifically tailored to serve as a feature store for machine learning workloads. Instead, feature stores typically require capabilities around versioning, time travel, and complex transformations which are not inherent to Neptune's feature set.

    However, for the sake of an educational exercise, let's assume that you want to use AWS Neptune as a part of your data infrastructure for a machine learning application, perhaps as a component of a larger feature store architecture to handle specific graph-related use cases within your features.

    Below is a Pulumi program written in Python that creates a minimal AWS Neptune setup, which could conceptually be part of a machine learning feature store system. We'll use Neptune to store graph data that might represent entities and relationships used in machine learning algorithms.

    First, you would define an instance of Neptune which would serve as the core of your graph database. Then, we can define a cluster instance that pertains to this cluster, which is where your actual data will reside.

    Take note that in a real-world scenario, you would also need to consider security (such as VPCs, subnet groups, and security groups), scalability, monitoring, and integration with data pipelines or other components of your ML infrastructure. Those are beyond the scope of this example.

    Let's begin with the Pulumi program:

    import pulumi import pulumi_aws as aws # Create a new Neptune Cluster # This is where metadata about your Neptune instances and cluster configuration will be managed neptune_cluster = aws.neptune.Cluster("neptuneMLFeatureStore", engine="neptune", engine_version="", # Ensuring encryption at-rest using KMS key storage_encrypted=True, # IAM Roles can be associated here for Authentication purposes iam_roles=[], # Defining the backup policy for the cluster backup_retention_period=7, preferred_backup_window="07:00-09:00", skip_final_snapshot=True) # Now, let's define a Cluster Instance which represents one of potentially many instances in our cluster neptune_cluster_instance = aws.neptune.ClusterInstance("neptuneMLFeatureStoreInstance", apply_immediately=True, cluster_identifier=neptune_cluster.id, instance_class="db.r4.large", engine="neptune", engine_version="", promotion_tier=0) # Finally, we shall export the cluster endpoint which applications can use to connect # to our Neptune database. This could be used by applications to write or read feature data pulumi.export('neptune_cluster_endpoint', neptune_cluster.endpoint)

    In the above Pulumi program, you have defined a Neptune cluster with a single cluster instance within it. The aws.neptune.Cluster resource creates the cluster, where you define elements like the engine version and storage encryption. The aws.neptune.ClusterInstance represents one instance within that cluster, determined by instance_class and linked to the cluster with cluster_identifier.

    The storage_encrypted parameter when set to True ensures that the data stored in the Neptune database is encrypted using AWS Key Management Service (KMS). skip_final_snapshot being True is suitable for this example but in a production environment, you may want to set this to False to ensure you have a backup before deletion.

    The endpoint of the Neptune cluster is then exported. This endpoint is what your applications and services would use to connect to the Neptune cluster to perform operations against the graph database.

    This simplified example acts as a starting point. A complete feature store implementation would require several other AWS services and Pulumi resources, as well as application code to manage and utilize the machine learning features.