# headspace Published articles for headspace. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## How Headspace Scaled Identity and Minimized Security Risk with Auth0 CIAM on AWS DevFeed: [How Headspace Scaled Identity and Minimized Security Risk with Auth0 CIAM on AWS]() Original publisher: [Read original article]() Author: Headspace Published: 2021-12-21T22:41:06Z Content type: article Language: en Sources: [Stories by Headspace on Medium]() Topics: [Auth0](), [Amazon Web Services](), [Authentication](), [Authorization](), [identity and access management](), [Security]() Tags: [auth0](), [authentication](), [authorization](), [aws](), [headspace](), [identity-and-access-management](), [identity-management](), [mobile](), [security](), [security-risk-management]() ### AI overview The article promotes a webinar featuring Headspace, AWS, and Auth0 about scaling customer identity and reducing security risk. It describes Headspace's need for a secure identity solution for its large monthly user base and enterprise customers, and presents authentication, authorization, and identity and access management as services delivered with AWS and Auth0. ### Source excerpt George Torres, Director of Engineering at Headspace recently participated in an AWS and AWS Partner Auth0 webinar on scaling identity and minimizing security risk. You can use this link to register and watch the webinar. Delivering security and mindfulness at scale Headspace's mission is to improve the health and happiness of the world by providing guided meditations and mindfulness content through its mobile application. The company sought out a scalable and secure identity solution to support its millions of monthly active users and accommodate continued growth. Additionally, it needed to support its enterprise customers who were including Headspace services in their employee benefits packages. With Amazon Web Services (AWS) and AWS Partner Auth0, Headspace was able to achieve both. In this webinar, hear how to implement a frictionless digital experience with authentication and authorization as a service. Watch this webinar, and see how to: Focus on innovation instead of building and maintaining identity solutions Incorporate identity into any customer-facing application quickly, securely, and at scale Enable secure identity and access management across your environments Who should watch? CTOs, CIOs, CISOs, VPs of IT, IT Managers, IT Directors, IT Supervisors, Software Developers, Software Development Managers, System Architects, C-Level or LOB Managers, CEOs, CMOs, COOs, LOB Directors, Sr. Solution Architects, Sr. Security Architects, Sr. Security Engineers, Sr. Identity Engineers, IT Security Directors, Tech Leads, Engineering Managers, Technology Executives, Product Managers, Security Identity Managers, ITOps Webinar SpeakersClare Nelson Sr. Partner Development Manager, Amazon Web Services (AWS), Inc. Shiven Ramji Chief Product Officer, Auth0 George Torres Director of Engineering, Headspace How Headspace Scaled Identity and Minimized Security Risk with Auth0 CIAM on AWS was originally published in Headspace-engineering on Medium, where people are continuing the ## Mindful Experimentation: Evaluate Recommendation System Performance using A/B Testing at Headspace DevFeed: [Mindful Experimentation: Evaluate Recommendation System Performance using A/B Testing at Headspace]() Original publisher: [Read original article]() Author: Headspace Published: 2021-11-29T23:34:41Z Content type: article Language: en Sources: [Stories by Headspace on Medium]() Topics: [recommendation systems](), [A/B Testing](), [data](), [Machine Learning & Artificial Intelligence]() Tags: [a-b-testing](), [causal-inference](), [data-science](), [experimentation](), [headspace](), [machine-learning](), [product-experimentation](), [recommendation-system]() ### AI overview This article describes how Headspace evaluates its personalized Content Customizer recommendation system with online controlled experiments, or A/B tests. It explains why the full recommendation system should be assessed before production and outlines experiment design, including recommendation surfaces, supported platforms, and audience selection. ### Source excerpt Author: Rohan Singh Rajput. Rohan is a Senior Data Scientist at Headspace. He combines his passion for Machine Learning with Causal Inference to improve the mindfulness and meditation practices of Headspace users. "If you can't measure it, you can't improve it." -- Peter Drucker. Motivation Content Customizer is Headspace's personalized recommendation system. Content Customizer uses historical data to train its machine learning model and provide personalized recommendations to the users, helping them discover more relevant content. There are various components involved in building a recommendation system, and the ML model is only one of them. Therefore, it is essential to evaluate the effectiveness of the recommendation system as a whole before deploying it to production. Online Controlled Experiments help us to assess our system's impact with statistical evidence. Online Controlled Experiments, a.k.a A/B testing, are the gold standard for estimating causality with high probability. A data-driven decision-making culture helps estimate the measurement's uncertainty to refute the null hypothesis based on experimental data. Furthermore, a random assignment of the users into a control-treatment group allows us to safely ignore the unobserved factors and model the parameters as random variables1. Experiment Design The following components are required to design the experiment. Recommendation Surface Area: We have a total of three surface areas for this experiment: First, in the Today tab, we will use the last three slots to display ML recommendations. Figure 1: Dynamic Playlist on Today's Tab Second is the Hero module, which is the top banner area of the Meditate/Sleep/Focus/Move tabs. Lastly, we will be using the recommended sub-tabs that also have three slots each for ML-powered content. Figure 2: Hero and Recommended Module of other four tabs In total, we have 19 places available for the experiment. The platform for Recommendation: Headspace serves on multiple platform ## 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]() Original publisher: [Read original article]() Author: Headspace Published: 2021-10-18T19:08:11Z Content type: article Language: en Sources: [Stories by Headspace on Medium]() Topics: [Machine learning](), [recommendation systems](), [Cloud](), [Back end](), [Android](), [iOS](), [Web]() Tags: [ai](), [android](), [backend](), [cloud](), [headspace](), [ios](), [machine-learning](), [ml](), [notifications](), [push-notification](), [push-notifications](), [recommendations](), [web]() ### 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