Scalable personalization on Azure

Recommendations are a main revenue driver for many businesses and are used in different kinds of industries, including retail, news, and media. With the availability of large amounts of data, you can now provide highly relevant recommendations using machine learning.

There are two main types of recommendation systems: collaborative filtering and content-based. Collaborative filtering identifies similar patterns in customer behavior and recommends items that other similar customers have interacted with. Content-based recommendation uses information about the items to learn customer preferences and recommends items that share properties with items that a customer has previously interacted with. The approach described in this document focuses on the content-based recommendation system.

This example scenario shows how your business can use machine learning to automate content-based personalization for your customers. At a high level, we use Azure Databricks to train a model that predicts the probability a user will engage with an item. That model is deployed to production as a prediction service using Azure Kubernetes Service. In turn, you can use this prediction to create personalized recommendations by ranking items based on the content that a user is most likely to consume.

Relevant use cases

This scenario is relevant to the following use cases:

  • Content recommendations on a website or in a mobile application
  • Product recommendation on an e-commerce site
  • Displayed ad recommendation on a website


Scalable personalization architecture diagram

This scenario covers the training, evaluation, and deployment of a machine learning model for content-based personalization on Apache Spark using Azure Databricks. In this case, a model is trained with a supervised classification algorithm on a dataset containing user and item features. The label for each example is a binary value indicating that the user engaged with (for example, clicked) an item. This scenario covers a subset of the steps required for a full end-to-end recommendation system workload. The broader context of this scenario is based on a generic e-commerce website with a front end that serves rapidly changing content to its users. This website uses cookies and user profiles to personalize the content for that user. Along with user profiles, the website may have information about every item it serves to each user. Once that data is available, the following steps are executed to build and operationalize a recommendation system:

  1. The sets of distinct user and item data are preprocessed and joined, which results in a mixture of numeric and categorical features to be used for predicting user-item interactions (clicks). This table is uploaded to Azure Blob Storage. For demonstration purposes, the Criteo display advertising challenge dataset is used. This dataset matches the described anonymized table, as it contains a binary label for observed user clicks, 13 numerical features, and an additional 26 categorical features.
  2. The MMLSpark library provides the ability to train a LightGBM classifier on Azure Databricks to predict the click probability as a function of the numeric and categorical features that were created in the previous step. LightGBM is a highly efficient machine learning algorithm, and MMLSpark enables distributed training of LightGBM models over large datasets.
  3. The trained classifier is serialized and stored in the Azure Model Registry. With Azure Model Registry, you can store and organize different versions of the model (for example, based on newer data or different hyperparameters) within an Azure Machine Learning (Azure ML) Workspace.
  4. A serving script is defined using the MML Spark Serving library to provide predictions from the trained model.
  5. Azure ML is used to create a Docker image in the Azure Container Registry that holds the image with the scoring script and all necessary dependencies for serving predictions.
  6. Azure ML is also used to provision the compute for serving predictions. A Kubernetes cluster is configured using Azure Kubernetes Service (AKS) with the number of nodes needed to handle expected load. The virtual machine size can be adjusted based on the model's computation and memory requirements.
  7. The scoring service is deployed as a web service on the AKS cluster. The service provides an endpoint where user and item features can be sent to receive the predicted probability of a click for that user and item.


This architecture makes use of the following components:

  • Azure Blob Storage is a storage service optimized for storing massive amounts of unstructured data. In this case, the input data is stored here.
  • Azure Databricks is a managed Apache Spark cluster for model training and evaluation. We also use MMLSpark, a Spark-based framework designed for large-scale machine learning.
  • Azure Container Registry is used to package the scoring script as a container image, which is used to serve the model in production.
  • Azure Kubernetes Service is used to deploy the trained model to web or app services.
  • Azure Machine Learning service is used in this scenario to register the machine learning model and to deploy AKS.
  • Microsoft Recommenders is an open-source repository that contains utility code and samples. With this repository, users can start to build, evaluate, and operationalize a recommender system.



For training, you can scale Azure Databricks up or down based on the size of the data used and the compute necessary for model training. To scale, you can adjust the total number of cores or amount of memory available to the cluster. Just edit the number or type of Virtual Machines (VMs) used. The Criteo dataset contains 45.8 million rows in this example; it was trained in a few minutes on a cluster with 10 standard L8s VMs.

For deployment, you can scale the compute resources based on the expected load for the scoring service and latency requirements. The scoring service uses MML Spark Serving running separately on each node in the Kubernetes cluster. With this practice, you can seamlessly transfer the feature transformation and model prediction pipeline developed on Azure Databricks to the production side. It also removes the need to precompute scores for all possible user and item combinations, which might be difficult if you're using dynamic user features, such as time of day.


Machine learning tasks are split into two resource components: resources for training, and resources for production deployment. Resources required for training generally don't need high availability, as live production requests don't directly hit these resources. Resources required for serving need to have high availability to serve customer requests.

Training on Azure Databricks can happen on any one of the regions with a service level agreement (SLA) to support your needs. For production deployment, Azure Kubernetes Service is used to provide broad geographic availability with this SLA.


This scenario can use Azure Active Directory (Azure AD) to authenticate users to the Azure Databricks workspace and the Azure Kubernetes cluster. Permissions can be managed via Azure AD authentication or role-based access control.

Deploy this scenario


You must have an existing Azure account.


All the code for this scenario is available in the Microsoft Recommenders repository.

To run the notebooks for training and deploying the recommendation model on Azure Databricks:

  1. Create an Azure Databricks workspace from the Azure portal.
  2. Follow the setup instructions to install utilities from the Microsoft Recommenders repository on a cluster within your workspace.
    1. Include the --mmlspark option in the install script to have MMLSpark installed.
    2. Also, MMLSpark requires autoscaling to be disabled in the Cluster setup.
  3. Import the training notebook into your workspace. After logging into your Azure Databricks Workspace:
    1. Select Home on the left side of the workspace.
    2. Right-click whitespace in your home directory.
    3. Select Import.
    4. Select URL, and paste the following string into the text field:
    5. Select Import.
  4. Repeat step 3 for the operationalization notebook here:
  5. Select the notebook to open it, attach the configured cluster, and execute the notebook.


To better understand the cost of running this scenario on Azure, we provide a pricing estimator based on the following assumptions:

  • Training data is of the same scale as the example dataset used (45.8 million rows).
  • Training needs to happen daily to update the serving model.
  • Training will occur on Azure Databricks using a cluster provisioned with 12 VMs using L8s instances.
  • Training will take an hour, including feature processing and model training plus validation.
  • Azure Machine Learning service will be used to deploy the model to AKS with a small three-node cluster using D3 instances.
  • AKS cluster will autoscale as needed, resulting in two nodes per month being active on average.

To see how pricing differs for your use case, change the variables to match your expected data size and serving load requirements. For larger or smaller training data sizes, the size of the Databricks cluster can be increased or reduced, respectively. To handle larger numbers of concurrent users during model serving, the AKS cluster should be increased. For more information on scaling AKS to support latency and load requirements, review the operationalization notebook.

Next steps