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.
Genna Gliner is a machine learning engineer at Mindbody working on building ML products for our customers and consumers. Prior to Mindbody, Genna was a data science consultant at Oliver Wyman who specialized in providing machine learning solutions to her clients. Genna Gliner completed her Masters in operations research and financial engineering from Princeton University. Her dissertation work focused on developing statistical models and methods for high-dimensional genomic data. Genna was an organizer for WiML 2017 as the student liaison chair.
Brandon Davis is an Machine Learning Engineer at Mindbody. With a background in Software Engineering and API Development, he brings this experience to the MLOps space to lead the ML team's efforts in data engineering as well as deployment, monitoring and integration of ML Services at Mindbody.