Models don’t perform identically on different sub-groups of input data. So how should you go about measuring the performance of sub-groups? Let’s dive in and see how.

Best practices for data science and engineering teams, covering the fundamentals of efficient ML monitoring.

So how do you go about aligning business and ML to make sure your AI program is not at risk?

Let’s dive into part 2 of safely rolling out models to production, CD, and its online validation strategies – shadow model, A/B testing, multi-armed bandit, etc.

While it is true to say that AI is everywhere, this is especially accurate when it comes to AI for marketing. Every leading marketing team today knows that machine learning can dramatically help them boost their effectiveness and their impact. Whether it’s to identify and engage users who are most likely to convert, ensure that

In this blog, we look at how fraud detection solution vendors can leverage their ML monitoring solutions to boost the efficiency of their fraud and data science teams and deliver better service to their merchants.