Feature Store for Machine Learning with MongoDB Atlas

Question

Pulumi · Accepted Answer

Feature stores are critical components of machine learning infrastructure, used for storing and serving features to training and prediction pipelines. A feature store ensures that features used during training are also available for inference in production, helping to avoid training-serving skew.

MongoDB Atlas is a fully-managed cloud database that is well-suited for implementing a feature store thanks to its flexibility, scalability, and rich querying capabilities. To create a feature store for Machine Learning on MongoDB Atlas, you'd typically need to set up an Atlas cluster, configure the database and collections where your features will be stored, and possibly set up auditing for compliance and tracing of access to the feature data.

Here's a Pulumi program in Python to set up the basic infrastructure for a feature store using MongoDB Atlas. In this program, we'll deploy an Atlas cluster and configure it according to best practices for a feature store.

First, we need to import the required Pulumi packages for MongoDB Atlas and (if necessary) for the cloud provider where we'll be running our deployment.

```python
import pulumi
import pulumi_mongodbatlas as mongodbatlas

# Configurations for our MongoDB Atlas cluster that will act as a Feature Store
project_name = "ml-feature-store"
cluster_name = "ml-feature-cluster"
mongo_db_version = "4.4"
provider_name = "AWS"  # The cloud provider for the Atlas cluster, can be AWS, GCP, or Azure
region_name = "us-east-1"

# MongoDB Atlas Project creation
project = mongodbatlas.Project("project",
    org_id=mongodbatlas.get_organization().id,
    name=project_name,
)

# MongoDB Atlas Cluster creation
cluster = mongodbatlas.Cluster("cluster",
    project_id=project.id,
    name=cluster_name,
    cluster_type="REPLICASET",
    mongo_db_major_version=mongo_db_version,
    provider_name=provider_name,
    provider_backup_enabled=True,  # Enable cloud provider snapshots
    disk_size_gb=10,
    provider_instance_size_name="M10",  # This is the instance size for the cluster
    provider_region_name=region_name,
    auto_scaling_disk_gb_enabled=True,
)

# Export the connection string for the cluster for application use
# This is a sensitive value and should be treated as such in a production environment
pulumi.export("mongo_uri", cluster.connection_strings.apply(lambda cs: cs["standard"]))
```

Here's what's happening in this program:

1. We import the required `pulumi` and `pulumi_mongodbatlas` libraries.
2. Then we declare some configurations for our project and cluster using variables. These could be customized to fit the needs of the feature store you're setting up.
3. We create a MongoDB Atlas Project which acts as a container for our MongoDB Atlas resources, including the cluster.
4. After that, we set up a MongoDB Atlas Cluster, which is where the features will be stored. We specify the configuration for the cluster, including the MongoDB version, cloud provider, instance size, and region. We enable automatic scaling for disk size to accommodate growing data. We also enable provider backup to take snapshots of our cluster for backup purposes.
5. Lastly, we export the connection string which applications will use to write and read features from our feature store. This should be kept secure, especially in a production environment.

With this basic setup, you'll have a running MongoDB Atlas cluster that you can use as a feature store for a Machine Learning workload. You would then proceed to define your data schemas and set up your feature pipelines to populate this store.

Keep in mind that this is a simple setup and that a production-ready feature store may require more settings, like encryption at rest, fine-grained access control, integration with data processing pipelines, and so forth. Pulumi can help to manage all these aspects declaratively.