Today, many companies that have lots of data are still struggling to derive value from machine learning (ML) and data science investments. Why? Accessing the data may be difficult. Or maybe it’s poorly labeled. Or vital context is missing. Or there are questions around data integrity. Or standing up an ML service can be cumbersome and complex.
At Nuuly, we offer an innovative clothing rental subscription model and are continually evolving our ML solutions to gain insight into the behaviors of our unique customer base as well as provide personalized services. In this session, I’ll share how we used event streaming with Apache Kafka® and Confluent Cloud to address many of the challenges that may be keeping your organization from maximizing the business value of machine learning and data science. First, you’ll see how we ensure that every customer interaction and its business context is collected. Next, I’ll explain how we can replay entire interaction histories using Kafka as a transport layer as well as a persistence layer and a business application processing layer. Order management, inventory management, logistics, subscription management – all of it integrates with Kafka as the common backbone. These data streams enable Nuuly to rapidly prototype and deploy dynamic ML models to support various domains, including pricing, recommendations, product similarity, and warehouse optimization. Join us and learn how Kafka can help improve machine learning and data science initiatives that may not be delivered to their full potential.