Session
Empowering Progress: Building a Personalized Training Goal Ecosystem with Databricks
Overview
Experience | In Person |
---|---|
Type | Breakout |
Track | Data Lakehouse Architecture and Implementation |
Industry | Enterprise Technology, Health and Life Sciences |
Technologies | Apache Spark, MLFlow, Databricks Workflows |
Skill Level | Intermediate |
Duration | 40 min |
Tonal Trainer is the world’s most intelligent home gym, combining cutting-edge hardware and sensors with AI-powered software to deliver personalized fitness experiences. Members share needs with us through interviews and through social media platforms. One item that consistently came up was having difficulty measuring progress on the machine. We created and deployed a robust Training Goal (TG) ecosystem for our users. TG is a four-part solution:
- Creating eight new options for TG preferences so that we could better understand what users wanted to accomplish.
- Seven new TG metrics that accumulate weekly as users complete workouts.
- TG weekly targets, which set metric ranges for users to achieve.
- Enhanced work out details, which tell users how much of an effect the workout has toward each goal, better guiding them to workouts that help them achieve their goals.
Databricks enabled us to deploy each of these components by the feature launch deadline.
Session Speakers
Giuseppe Barbalinardo
/Senior Director, Data and AI
Tonal
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Kristi Korsberg
/Sr Manager, Data Science and AI
Tonal