We designed our system to fit the needs of data scientists and engineers, no matter which ML tasks they are tackling. Having said that, we’ve seen some use cases come up repeatedly within each industry.
As online shopping ramps up and brands strive to boost competitive differentiation, ML models enable dynamic pricing, localization, personalization, recommendations, self-learning chatbots, and more. In the back office, AI-based churn prediction, LTV prediction, fraud detection, and inventory management also play a critical role in customer retention and loss prevention.
However, with ever-changing market dynamics, consumer trends, and bad actors, eCommerce ML systems require constant tuning based on dependable analyses. Deepchecks continuously monitors the data and the model, detecting and alerting about integrity issues, data shifts, underperforming segments, and more.