Recent posts by Mona

Machine learning operations (MLOps) is currently one of the hottest areas for startup investment, because while best practices for building machine learning models are relatively well understood, a great deal of innovation is being poured into devising ways to best operationalize them for production. Chief among the MLOps categories is ML monitoring. Making sense of the landscape of ML monitoring tools can be frustrating, time consuming, and just plain confusing. Our goal with this article is to chart its cartography and, in doing so, hopefully illuminate some of the common pitfalls around choosing an appropriate monitoring solution, thereby bringing order to the chaos.

Trusting in artificial intelligence systems is not easy. Given the variety of edge cases on which machine learning models may fail, as well as the lack of visibility into the processes underlying their predictions and the difficulty of correlating their outputs to downstream business results, it’s no wonder that business leaders often look upon AI with some skepticism.

Data and concept drift are frequently mentioned in the context of machine learning model monitoring, but what exactly are they and how are they detected? Furthermore, given the common misconceptions surrounding them, are data and concept drift things to be avoided at all costs or natural and acceptable consequences of training models in production? Read on to find out. In this article we will provide a granular breakdown of model drift, along with methods for detecting them and best practices for dealing with them when you do.

Machine learning doesn’t stop at deployment — in fact, that’s where the real work begins. Unlike traditional software, ML models are living systems that constantly interact with new data, evolve over time, and influence real-world outcomes. Without robust monitoring, these models are prone to performance degradation, silent failures, and misalignment with business goals.

Yet, despite its importance, ML monitoring is often treated as an afterthought — or worse, reduced to superficial dashboards and basic alerts. Not all monitoring tools are created equal, and choosing the right one can mean the difference between a model that quietly drifts off-course and one that drives sustained, measurable value.

In this post, I’ll break down the three non-negotiables every effective ML monitoring solution must have. Whether you’re deciding to build a system internally or evaluating a vendor, these core principles will help guide your approach to ensuring your models remain accurate, relevant, and aligned with business objectives over time.