# Explainable and Accessible AI: Using Push Notifications to Broaden the Reach of ML at Headspace

DevFeed: [Explainable and Accessible AI: Using Push Notifications to Broaden the Reach of ML at Headspace](<https://devfeed.tech/articles/explainable-and-accessible-ai-using-push-notifications-to-broaden-the-reach-of-ml-at-headspace-24573.md>)

Original publisher: [Read original article](<https://medium.com/headspace-engineering/explainable-and-accessible-ai-using-push-notifications-to-broaden-the-reach-of-ml-at-headspace-a03c7c2bbf06?source=rss-3da90e297190------2>)

Author: Headspace

Published: 2021-10-18T19:08:11Z

Content type: article

Language: en

Sources: [Stories by Headspace on Medium](<https://devfeed.tech/sources/stories-by-headspace-on-medium.md>)

Topics: [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [recommendation systems](<https://devfeed.tech/topics/recommendation-systems.md>), [Cloud](<https://devfeed.tech/topics/cloud.md>), [Back end](<https://devfeed.tech/topics/backend.md>), [Android](<https://devfeed.tech/topics/android.md>), [iOS](<https://devfeed.tech/topics/ios.md>), [Web](<https://devfeed.tech/topics/web.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [android](<https://devfeed.tech/tags/android.md>), [backend](<https://devfeed.tech/tags/backend.md>), [cloud](<https://devfeed.tech/tags/cloud.md>), [headspace](<https://devfeed.tech/tags/headspace.md>), [ios](<https://devfeed.tech/tags/ios.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [ml](<https://devfeed.tech/tags/ml.md>), [notifications](<https://devfeed.tech/tags/notifications.md>), [push-notification](<https://devfeed.tech/tags/push-notification.md>), [push-notifications](<https://devfeed.tech/tags/push-notifications.md>), [recommendations](<https://devfeed.tech/tags/recommendations.md>), [web](<https://devfeed.tech/tags/web.md>)

## AI overview

Headspace describes infrastructure that combines machine-learning recommendations, cloud services, and Braze to deliver personalized content through push notifications to members who have not recently opened the app. The article explains the limitations of relying on in-app access and says the initial experiment was implemented as an evergreen production model.

## Source excerpt

Author: Matt Linder / Co-Author: Koyuki Nakamori TL;DR Headspace wanted to serve personalized content to members who had not recently opened the app We built out infrastructure that allowed us to leverage our Cloud services, plus the 3rd party Braze Customer Engagement platform, to serve Machine Learning-recommended content to members via push notifications Our first experiment doing so got GREAT results We implemented that experiment as an evergreen model in production You should, too Introduction and Problem Statement Headspace's core products are iOS, Android, and web-based apps that focus on improving the health and happiness of their users through mindfulness, meditation, sleep, exercise, and focus content. Machine learning models are core to our user experiences by offering recommendations that engage our users with relevant, personalized content that builds consistent habits in their lifelong journey. Headspace also has an amazing Lifecycle marketing team, who do incredible work in providing regular communications -- through email, push notifications, in-app modals, and other surfaces -- that further our users' engagement, helping them on their journey from prospects to members to advocates. Until recently, those two aspects -- Machine Learning and the out-of-app communication channels served by Lifecycle- were totally separate. Traditionally, users consume our ML models' personalized recommendations by Navigating to one of the app's tabs / views that contains content (for example, our Today tab). Sending a request from the app client to ask our backend content services to load content to serve users. Our content services then forward the request to our Prediction Service, which supplies a content recommendation for that user (or, if none is available, generates a default fallback content to serve). The Problem This approach has three primary limitations: It isn't able to directly respond to the immediate user's context. For instance, if a user has recently sear