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Quickstart (Azure)

A quick start guide for Featureform on Azure AKS.
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

Requirements

  • 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.
export FEATUREFORM_HOST=<your_domain_name>

Step 3: Setup the AKS Cluster

This step will provision a single node Kubernetes cluster with AKS

Login

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 a 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 https://storage.googleapis.com/featureform-helm/
helm repo add jetstack https://charts.jetstack.io
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 \
--create-namespace
helm install featureform featureform/featureform \
--set global.publicCert=true \
--set global.localCert=false \
--set global.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:
Key
Value
Record Type
<your_domain_name>
<ingress_ip_address>
A
<your_domain_name>
0 issuewild "letsencrypt.org"
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.
definitions.py
import featureform as ff
redis = ff.register_redis(
name = "redis-quickstart",
host="quickstart-redis", # The internal dns name for redis
port=6379,
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
port="5432",
user="postgres",
password="password",
database="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 definitions.py

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.
definitions.py
ff.register_user("featureformer").make_default_owner()
Now we'll register our user fraud dataset in Featureform.
definitions.py
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.
definitions.py
@postgres.sql_transformation(variant="quickstart")
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.
definitions.py
user = ff.register_entity("user")
# Register a column from our transformation as a feature
average_user_transaction.register_resources(
entity=user,
entity_column="user_id",
inference_store=redis,
features=[
{"name": "avg_transactions", "variant": "quickstart", "column": "avg_transaction_amt", "type": "float32"},
],
)
# Register label from our base Transactions table
transactions.register_resources(
entity=user,
entity_column="customerid",
labels=[
{"name": "fraudulent", "variant": "quickstart", "column": "isfraud", "type": "bool"},
],
)
Finally, we'll join together the feature and label intro a training set.
definitions.py
ff.register_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 definitions.py

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"})
print(fpf)