We are happy to announce a new deployment of Databricks in the financial technology sector with LendUp, a company that builds technology to expand access to credit. LendUp uses Databricks to develop innovative machine learning models that touch all aspects of its lending business. Specifically, it uses Databricks to perform feature engineering at scale and quickly iterate through the model building process. Faster iterations lead to more accurate models, the ability to offer credit to more of the tens of millions of Americans who need it and the ability to establish new products more easily.
Before Databricks, LendUp performed feature extraction on a single machine in AWS. Processing millions of semi-structured documents took multiple days, which delayed updates to critical machine learning models. The productivity of the LendUp team was further impeded by the poor integration of data storage, feature extraction, modeling, and analytics tools, requiring the team to build custom solutions to bring together disparate data sources and analytics tools.
Databricks enables LendUp to speed up feature extraction by replacing the single instance machine with highly tuned Apache Spark clusters. Databricks also provided the critical capabilities to build sophisticated machine learning models in an integrated workspace, replacing a host of disjointed tools and eliminating the need to maintain expensive custom solutions.
As a result of deploying Databricks, LendUp can more readily advance their core mission. LendUp’s credit models now include more diverse and larger volumes of semi-structured data, and could be generated within a shorter amount of time.
Download this case study to learn more about how LendUp is using Databricks.
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