From e-books, to use cases and research, here’s everything you need effectively assure the health of your models in production.
Fundamentals for efficient ML model monitoring
This ebook provides a framework for anyone who has an interest in building, testing, and implementing a robust monitoring strategy in their organization or elsewhere.
ML monitoring for food delivery
Answering the question: “how many orders can a carrier deliver within an hour?” is a starting point for customer satisfaction, for carrier satisfaction, and for the profitability of the company.
ML monitoring for gaming
Leveraging over billions of data points to influence the decisions that shape and constantly improve cutting-edge games, applying machine learning to the gaming space is a complex play.
Using Superwise at monday.com
As the top-rated Work OS platform, monday.com needs to know what gives users a memorable experience. The service offers a 14-day free trial for new potential subscribers, a crucial window in which they must identify likely subscribers, ensure customer value is realized, and prevent churn. Utilizing ML technology in its model, monday.com customizes workflow systems for a better fit. But it was taking at least 21 days to detect prediction degradation and another 7 to 14 days to troubleshoot and implement a solution.
ML monitoring customer lifetime value
As marketing use cases are likely to triple in the next two years, organizations are required to foster more trust around their AI programs.
ML monitoring for credit score & underwriting
The use of AI for underwriting creates opportunities to win more business and to reduce risk by leveraging new sources of data previously unavailable in traditional credit scoring, and by identifying hidden patterns.
ML monitoring for fraud detection
From rapidly changing fraudster attack types to misidentified but legitimate customer behavior and through shifting market dynamics that span across industries and geographies, the fraud detection space is constantly changing.