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.
With the advertising industry built around massive volumes of data, it has been one of the earliest adopters regarding machine learning. ML systems are used to simplify and automate audience segmentation, message personalization, and campaign management. They also enable dynamic creative optimization (adapting color, design, and layout for each viewer), predictive bidding, anomaly detection, detection of bots and ad fraud, and more. With a rapidly changing landscape and skills shortage, many companies face challenges when it comes to optimizing ad spend and maximizing returns. Continuously validating these data-driven systems, Deepchecks provides numerous alerts in real-time insights, enabling organizations to understand and adapt to market trends, detecting issues early on, and increasing ROAS (Return On Advertising Spend).
As AI adoption increases within financial institutions, machine learning systems are taking an increasingly large role in their underlying infrastructure. However, in many cases, there is a trade-off between rolling out different versions of these ML systems quickly and maintaining control over them and their behavior. Deepchecks offers a comprehensive solution for continuous validation, enabling the ongoing inspections of the data and the models during a few different phases: pre-launch, production, and re-training. It’s a bit counter-intuitive, but once organizations have these types of guardrails and audit mechanisms in place, they can actually innovate faster!
In many cases, an organization creating an ML solution for a different party doesn’t fully control the data being processed. While they are still held accountable for the quality of their systems’ output, the owners or controllers of the data don’t always bother to let them know about gradual or sudden changes in the data. Deepchecks offers a comprehensive solution for continuous validation of this type of system so that you get real-time alerts about distribution changes, changes in the data scheme, data integrity issues, and more. Using Deepchecks, you can increase the control you have over your ML system, and ensure you provide high-quality service to your clients, even if the data is controlled by someone else!