Azure
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
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Python 3.7-3.10
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Kubectl
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Azure CLI
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An available domain/subdomain name
Install the Featureform SDK via Pip.
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
Login to the Azure CLI
Create Resource Group
Create a resource group for the kubernetes cluster
Create A Cluster
Create a single node cluster for Featureform
Add To Kubeconfig
Add the cluster information to the kubeconfig as the current context
Verify connection
You should get a result like:
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.
Now we can install the Helm charts.
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.
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
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.
Once we create our config file, we can apply it to our Featureform deployment.
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.
Next, we’ll define a SQL transformation on our dataset.
Next, we’ll register a passenger entity to associate with a feature and label.
Finally, we’ll join together the feature and label into a training set.
Now that our definitions are complete, we can apply them to our Featureform instance.
Step 7: Serve features for training and inference
Once we have our training set and features registered, we can train our model.
We can serve features in production once we deploy our trained model as well.