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Productionizing Machine Learning in Our Health and Wellness Marketplace

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We build machine learning products to support discovery and automation within the fitness health and wellness sector. Our products range from building recommender systems to enable our consumers to discover products from our customers within our fitness marketplace to applying natural language techniques to enable our customers to create automated marketing emails to delight their customers. In this talk, we will present our solution for training and deploying machine learning models into our production environment. We will talk about how our pipeline has evolved with open source tools like DBT, AirFlow and Sagemaker to address various pain points in building and scaling our data pipelines to support our machine learning solutions across the breadth of our wellness and beauty product ecosystem.

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Productionizing Machine Learning in Our Health and Wellness Marketplace

  1. 1. Productionizing Machine Learning In Our Health And Wellness Marketplace D ATA + A I S U M M I T, M AY 2 4 TH- 2 8 TH B R A N D O N D AV I S A N D G E N N A G L I N E R
  2. 2. Agenda 1) AIML at Mindbody 2) Recommendation Systems for the Health and Wellness Marketplace 3) Productionization Pain Points 4) Streamlined Solution
  3. 3. • Mindbody is the premier software provider and marketplace for the health and wellness industry • The AIML team was established in 2019 to build new tools and services powered by AIML to complement our marketplace and business offerings Connecting the World to Wellness
  4. 4. B2B SaaS Mindbody creates products for health and wellness practitioners that help them run their businesses. AIML features include: • Automated marketing campaigns powered by NLP • Lead Scoring • Churn Prediction • Automated Business Insights • Onboarding Concierge M I N D B O D Y P R O D U C T S
  5. 5. Consumer Marketplace • Recommendation Engines • Personalized Search • Personalized Campaigns • Trending and Trust Signals • AI Wellness Coach M I N D B O D Y P R O D U C T S
  6. 6. Marketplace Recommender “I want lifting at studio A.” “I want yoga at studio B.” “HIIT is complimentary to lifting…” “How about this highly rated lifting class?” “Because you like meditation…you might like…” “A lot of yogi’s enjoy this lifting class!”
  7. 7. Dynamic Pricing Recommender Designed to bring the right class offerings to the right consumers to increase conversion General Marketplace Recommender We want to reuse these components to build a personalized recommender for our in- person and virtual offerings We want to build this rapidly IN PRODUCTION IN DEVELOPMENT Our Engines Collaborative Filter Content Based Recommender Ensemble Recommender Virtual Class Recommender Multiple engines designed to surface a greater variety of inventory to drive discovery Studio Collaborative Filter Category Collaborative Filter Use case 1 Consumer Preferences Use Case 2 Use Case 3 9 months 5 months
  8. 8. What goes into an ML Project? Google Cloud - MLOps: Continuous delivery and automation pipelines in machine learning Data Collection Configuration Automation Data Verification Feature Engineering Metadata management Testing and debugging Resource Management Serving Infrastructure Monitoring ML Code Model Analysis Process Management
  9. 9. Key Components for Successful ML ML Code Data Infrastructure UI/UX
  10. 10. P A I N P O I N T S Recommender systems Training Data Prediction Features Recommender API 1 Model Training A Training Data Prediction Features Recommender API 2 Model Training B Model Training C Model Training A 1. Duplicated datasets and feature engineering efforts 2. Redundant models with no sharing mechanism 3. Recurring jobs are non- standardized and difficult to manage, with low visibility.
  11. 11. Duplicated datasets and feature engineering efforts PAIN POINT 1 Feature Store The Feature Store is an ecosystem of databases, queries and code to provide ML pipelines with unified access to features for machine learning. Consists of two databases: • Offline Feature Store – model training • Online Feature Store – model serving Key Result: Standardized access to features and data preprocessing directly from our API code making it simple to re-use features and move code from notebooks to repos. SOLUTION
  12. 12. Recommender Arch. with Feature Store Recommender 2 Recommender 1 Online FS Offline Feature Store Online FS DB Online FS API Model Training B Model Training C Recommender API 2 Recommender API 1 Model Training A Data Warehouse Model Training A
  13. 13. Redundant models across systems with no sharing mechanism PAIN POINT 2 SOLUTION Model Registry The Model Registry is a centralized model store to collaboratively manage the full model lifecycle of an MLflow Model. It provides model lineage, model versioning, stage transitions (for example from staging to production), and annotations. Key Result: We can share models across services while maintaining traceability and performance
  14. 14. Recommender Arch. with Model Registry Model Training Online FS Offline Feature Store Online FS DB Online FS API Model Training B Model Training C Recommender API 2 Recommender API 1 Model Training A Data Warehouse Model Registry
  15. 15. Recurring jobs are non-standardized and difficult to manage, with low visibility PAIN POINT 3 SOLUTION Centralized Orchestration Tool to programmatically author, schedule and monitor workflows written as pipelines represented as directed acyclic graphs (DAGs) Get fresh data Retrain Models Compute Metrics Execute query AWS Sagemaker processing job Accuracy, Precision, MAP@k, MAR@k
  16. 16. Recommender Arch. with Orchestration Model Training Online FS Offline Feature Store Online FS DB Online FS API Model Training B Model Training C Recommender API 2 Recommender API 1 Model Training A Data Warehouse Model Registry Orchestration
  17. 17. Key Takeaways By taking the time to invest in our ML Ops framework using free open-source tools, we were able to achieve: 1) Better Maintainability 2) Centralized, Consistent, and Improved Quality Data 3) Reusable and Scalable Shared Components
  18. 18. Key result: Product completion time 9 months 5 months 1 months Product completion time reduced by over 85%
  19. 19. Thank you. Your feedback is important to us. Don’t forget to rate and review the session Join our team! Reach out to recruiting@mindbodyonline.com

We build machine learning products to support discovery and automation within the fitness health and wellness sector. Our products range from building recommender systems to enable our consumers to discover products from our customers within our fitness marketplace to applying natural language techniques to enable our customers to create automated marketing emails to delight their customers. In this talk, we will present our solution for training and deploying machine learning models into our production environment. We will talk about how our pipeline has evolved with open source tools like DBT, AirFlow and Sagemaker to address various pain points in building and scaling our data pipelines to support our machine learning solutions across the breadth of our wellness and beauty product ecosystem.

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