Step 1: Install Featureform client


  • Python 3.7-3.10

  • Kubectl

  • Azure CLI

  • An available domain/subdomain name

Install the Featureform SDK via Pip.

pip install featureform

Step 2: Export domain name

Featureform uses gRPC which, in combination with the nginx ingress requires a fully qualified domain name.


Step 3: Setup the AKS Cluster

This step will provision a single node Kubernetes cluster with AKS


Login to the Azure CLI

az login

Create Resource Group

Create a resource group for the kubernetes cluster

az group create --name FeatureformResourceGroup --location eastus

Create A Cluster

Create a single node cluster for Featureform

az aks create --resource-group FeatureformResourceGroup --name FeatureformAKSCluster --node-count 1 --generate-ssh-keys

Add To Kubeconfig

Add the cluster information to the kubeconfig as the current context

az aks get-credentials --resource-group FeatureformResourceGroup --name FeatureformAKSCluster

Verify connection

kubectl get nodes

You should get a result like:

NAME                                STATUS   ROLES   AGE     VERSION
aks-nodepool1-25554489-vmss000000   Ready    agent   7m56s   v1.24.6

Step 4: 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

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 publicCert=true \
    --set selfSignedCert=false \
    --set hostname=$FEATUREFORM_HOST
helm install quickstart featureform/quickstart

Step 5: Setup Domain Name

Get the ingress IP address

Get the IP address of the ingress. It may take a minute or so to show.

kubectl get ingress

In your DNS provider create two records:

KeyValueRecord Type
<your\_domain\_name>0 issuewild “”CAA

This will allow the client to securely connect to the cluster by allowing the cluster to provision its own public IP address.

You can check when the cluster is ready by running

kubectl get cert

and checking that the status of the certificates is ready.

Step 6: 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 7: 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.
user = ff.register_entity("user")
# Register a column from our transformation as a feature
        {"name": "avg_transactions", "variant": "quickstart", "column": "avg_transaction_amt", "type": "float32"},
# Register label from our base Transactions table
        {"name": "fraudulent", "variant": "quickstart", "column": "isfraud", "type": "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 them 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"})