A quick start guide for Featureform on AWS EKS.
This quickstart will walk through creating a few simple features, labels, and a training set using Postgres and Redis. We will use a transaction fraud training set.

Step 1: Install Featureform client


  • Python 3.7+
Install the Featureform SDK via Pip.
pip install featureform

Step 2: Deploy EKS

You can follow our Minikube or Kubernetes deployment guide. This will walk through a simple AWS deployment of Featureform with our quick start Helm chart containing Postgres and Redis.
Install the AWS CLI and eksctl then run the following command to create an EKS cluster.
eksctl create cluster \
--name featureform \
--version 1.24 \
--region us-east-1 \
--nodegroup-name linux-nodes \
--nodes 1 \
--nodes-min 1 \
--nodes-max 4 \
--with-oidc \
Newer versions of eksctl require you to separately add a Container Storage Interface (CSI) driver to support Persistent Volume Claims. For complete details on adding the Amazon EBS CSI driver to your EKS cluster, see Managing the Amazon EBS CSI driver as an Amazon EKS add-on; however, the below examples should allow for a simple deployment.

Create an Amazon EBS CSI Driver IAM Role

eksctl create iamserviceaccount \
--name ebs-csi-controller-sa \
--namespace kube-system \
--cluster featureform \
--region us-east-1 \
--attach-policy-arn arn:aws:iam::aws:policy/service-role/AmazonEBSCSIDriverPolicy \
--approve \
--role-only \
--role-name AmazonEKS_EBS_CSI_DriverRole

Create the Amazon EBS CSI Add-On

To easily find the account ID you used to create the cluster, run:
aws sts get-caller-identity --query Account --output text
Then, to add the Amazon EBS CSI add-on, run:
eksctl create addon \
--name aws-ebs-csi-driver \
--cluster featureform \
--region us-east-1 \
--service-account-role-arn arn:aws:iam::<AWS ACCOUNT ID>:role/AmazonEKS_EBS_CSI_DriverRole \

Step 3: Install Helm charts

We'll be installing three Helm Charts: Featureform, the Quickstart Demo, and Certificate Manager.
First we need to add the Helm repositories.
helm repo add featureform
helm repo add jetstack
helm repo update
Prior to installing the Helm charts, export your FEATUREFORM_HOST value:
Now we can install the Helm charts.
helm install certmgr jetstack/cert-manager \
--set installCRDs=true \
--version v1.8.0 \
--namespace cert-manager \
helm install featureform featureform/featureform \
--set global.publicCert=true \
--set global.localCert=false \
--set global.hostname=$FEATUREFORM_HOST
helm install quickstart featureform/quickstart

Step 4: Register providers

The Quickstart helm chart creates a Postgres instance with preloaded data, as well as an empty Redis standalone instance. Now that they are deployed, we can write a config file in Python.
import featureform as ff
redis = ff.register_redis(
name = "redis-quickstart",
host="quickstart-redis", # The internal dns name for redis
description = "A Redis deployment we created for the Featureform quickstart"
postgres = ff.register_postgres(
name = "postgres-quickstart",
host="quickstart-postgres", # The internal dns name for postgres
description = "A Postgres deployment we created for the Featureform quickstart"
Once we create our config file, we can apply it to our Featureform deployment.
featureform apply

Step 6: Define our resources

We will create a user profile for us, and set it as the default owner for all the following resource definitions.
Now we'll register our user fraud dataset in Featureform.
transactions = postgres.register_table(
name = "transactions",
variant = "kaggle",
description = "Fraud Dataset From Kaggle",
table = "Transactions", # This is the table's name in Postgres
Next, we'll define a SQL transformation on our dataset.
def average_user_transaction():
"""the average transaction amount for a user """
return "SELECT CustomerID as user_id, avg(TransactionAmount) " \
"as avg_transaction_amt from {{transactions.kaggle}} GROUP BY user_id"
Next, we'll register a passenger entity to associate with a feature and label.
class User:
# Register a column from our transformation as a feature
avg_transactions = ff.Feature(
average_user_transaction[["user_id", "avg_transaction_amt"]], # We can optional include the `timestamp_column` "timestamp" here
# Register label from our base Transactions table
fraudulent = ff.Label(
transactions[["customerid", "isfraud"]], variant="quickstart", type=ff.Bool
Finally, we'll join together the feature and label into a training set.
"fraud_training", "quickstart",
label=("fraudulent", "quickstart"),
features=[("avg_transactions", "quickstart")],
Now that our definitions are complete, we can apply it to our Featureform instance.
featureform apply

Step 7: Serve features for training and inference

Once we have our training set and features registered, we can train our model.
import featureform as ff
client = ff.ServingClient()
dataset = client.training_set("fraud_training", "quickstart")
training_dataset = dataset.repeat(10).shuffle(1000).batch(8)
for row in training_dataset:
print(row.features(), row.label())
We can serve features in production once we deploy our trained model as well.
import featureform as ff
client = ff.ServingClient()
fpf = client.features([("avg_transactions", "quickstart")], {"user": "C1410926"})
# Run features through model