tutorial

Getting started with Feast on Azure

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

Prerequisites

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.

1. Deploy Infrastructure

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:

az ad signed-in-user show --query objectId -o tsv

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

• install the feast 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.

2. Git clone this repo to your compute instance

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/Azure/feast-azure

3. Load feature values into Feature Store

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

4. Register features in Feature store

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

5.Train and Deploy a model using the Feature Store

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