Machine Learning and MLOps for Home Value Adjustment

Company Overview

Redfin is a high-tech customer-first real estate brokerage firm for buying and selling homes. By combining its own full-service agents with modern technology they redefine real estate in the consumer’s favor. Homebuyers and sellers enjoy a full-service, technology-powered experience from Redfin real estate agents, while saving thousands in commissions.

Situation Overview

Redfin wanted to estimate the value of homes by including both image data and text into the assessment of a property. For example, when a user uploads an image of a property, a Machine Learning model can integrate a set of real estate features like the presence of a pool, fireplace, etc. to value the property.

Solution

SpringML developed Machine Learning pipelines, improved and automated Machine Learning practices on Google Cloud and Kubeflow. By structuring an MLOps practice with Redfin including CI/CD/CT infrastructure, the data science and IT teams collaborate and increase the pace of ML model development, training, and deployment to
production. 

Results

By automating many ML models and deployment practices, the Redfin data science team is developing and deploying more ML models to the Redfin platform thus improving the customer experience. The Redfin team is looking at 3 other use cases to expand the use of their ML platform with Kubeflow.

Thought Leadership

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