![]() Now, using Amazon SageMaker and machine learning, the company can more effectively match customers to the right financial products for them. ![]() Previously, NerdWallet provided customers with a list of potential credit cards, but it had no way to forecast the likelihood of acceptance. NerdWallet’s first project leveraging the new approach was a recommendations platform powered by TensorFlow. “We’re providing a guided path that makes solving these surrounding infrastructure problems easier from a platform and engineering perspective, while also accelerating the work of our data scientists. “Amazon SageMaker makes it easy for our data scientists to become the core owners and drivers of their work, rather than having multiple handoffs and having to re-implement everything,” says Kirkman. The new solution also helped the company remove roadblocks and speed time-to-delivery. “That wouldn’t have been possible otherwise.” “We essentially unlocked business value in two months,” says Kirkman. “Amazon SageMaker basically provided us with machine learning as a service,” says Kirkman.Īdopting Amazon SageMaker enabled NerdWallet to quickly modernize its data science engineering practices. With this fully managed service, the company could leverage underlying Amazon EC2 instances, including Amazon EC2 P3 instances with NVIDIA V100 Tensor Core GPUs, and their existing Amazon ECS image-building pipeline to reduce the time it takes to train ML models. The team decided to add Amazon SageMaker to the mix. ![]() NerdWallet was already using a number of Amazon Web Services (AWS) solutions, including Amazon Elastic Compute Cloud (Amazon EC2) and Amazon Elastic Container Service (Amazon ECS). “The key question for a startup is ‘how do we add business value fastest?’ We wanted a machine learning platform like some of the big companies had, but we weren't in a position to invest much in it,” says Krishnamurthy. “Reducing the feedback loop would significantly improve our ability to execute on data science projects.”Īs a startup, NerdWallet didn’t have the resources required to reinvent the wheel. “The faster we could ship models to production, the faster our data science team could iterate on those models and the better we could make our product experience,” says Kirkman. The company needed to solve its engineering plumbing problems so its data scientists could train ML models more effectively, speed up the process from concept to delivery, and focus more on high-value projects. “Our data scientists had to install things by hand and deal with whatever environment the last data scientist had left on the machine,” says Sharadh Krishnamurthy, staff software engineer at NerdWallet. He says, “It would take months to go from prototype to production and there were many inefficiencies along the way.”Īt the time, NerdWallet data scientists used a largely manual approach to managing ML libraries, which wasn’t optimal from a cost or workflow perspective. “We realized early on that data science was going to be essential to building a more personal product and user experience,” says Ryan Kirkman, senior engineering manager at NerdWallet.Īs the company’s engineering team began to deploy its first ML models to production, Kirkman and his team found that the process took much longer than expected. The company relies heavily on data science and machine learning (ML) to connect customers with personalized financial products. NerdWallet is a personal finance startup that provides tools and advice that make it easy for customers to pay off debt, choose the best financial products and services, and tackle major life goals like buying a house or saving for retirement.
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