The objective of this tutorial is to build a model that predicts if a driver will complete a trip based on a number of features ingested into Feast. During this tutorial you will:

1. Deploy the infrastructure for a feature store (using an ARM template)
2. Register features into a central feature registry hosted on Blob Storage
3. Consume features from the feature store for training and inference

For this tutorial you will require:

1. An Azure subscription.
2. Working knowledge of Python and ML concepts.
3. Basic understanding of Azure Machine Learning - using notebooks, etc.

We have created an ARM template that deploys and configures all the infrastructure required to run feast in Azure. This makes the set-up very simple - select the Deploy to Azure button below.

The only 2 required parameters during the set-up are:

• Admin Password for the the Dedicated SQL Pool being deployed.
• Principal ID this is to set the storage permissions for the feast registry store. You can find the value for this by opening Cloud Shell and run the following command:

# If you are using Azure portal CLI or Azure CLI 2.37.0 or above
az ad signed-in-user show --query id -o tsv

# If you are using Azure CLI below 2.37.0
az ad signed-in-user show --query objectId -o tsv

You may want to first make sure your subscription has registered Microsoft.Synapse, Microsoft.SQL, Microsoft.Network and Microsoft.Compute providers before running the template below, as some of them may require explicit registration. If you are on a Free Subscription, you will not be able to deploy the workspace part of this tutorial.

The ARM template will not only deploy the infrastructure but it will also:

• install feast with the azure provider on the compute instance
• set the Registry Blob path, Dedicated SQL Pool and Redis cache connection strings in the Azure ML default Keyvault.

☕ It can take up to 20 minutes for the Redis cache to be provisioned.

In the Azure Machine Learning Studio, navigate to the left-hand menu and select Compute. You should see your compute instance running, select Terminal

In the terminal you need to clone this GitHub repo:

git clone https://github.com/feast-dev/feast

In the Azure ML Studio, select Notebooks from the left-hand menu and then open the Loading feature values into feature store notebook.Work through this notebook.

💁Ensure the Jupyter kernel is set to Python 3.8 - AzureML

In the Azure ML Studio, select Notebooks from the left-hand menu and then open the register features into your feature registry notebook. Work through this notebook.

💁Ensure the Jupyter kernel is set to Python 3.8 - AzureML

In the Azure ML Studio, select Notebooks from the left-hand menu and then open the train and deploy a model using feast notebook. Work through this notebook.

💁Ensure the Jupyter kernel is set to Python 3.8 - AzureML

If problems are encountered during model training stage, create a new cell and rexecute !pip install scikit-learn==0.22.1. Upon completion, restart the Kernel and start over.

• If you are on a free tier instance, you will not be able to deploy the azure deployment because the azure workspace requires VCPUs and the free trial subscription does not have a quota.
• The workaround is to remove the Microsoft.MachineLearningServices/workspaces/computes resource from fs_synapse_azure_deploy.json and setting up the environment locally.
  1. After deployment, find your Azure SQL Pool secrets by going to Subscriptions-><Your Subscription>->Resource Group->Key Vault and giving your account admin permissions to the keyvault. Retrieve the FEAST-REGISTRY-PATH, FEAST-OFFLINE-STORE-CONN, and FEAST-ONLINE-STORE-CONN secrets to use in your local environment.
  2. In your local environment, you will need to install the azure cli and login to the cli using az login.
  3. After everything is setup, you should be able to work through the first 2 tutorial notebooks without any errors (The 3rd notebook requires Azure workspace resources).