Recent posts by Mona

Machine Learning Monitoring Overview 

Machine learning monitoring is a crucial aspect that allows you to develop insights into your ML models in production and ensure that they’re performing as intended. AI monitoring is also a necessary precondition to extracting meaningful business value from your models. Without understanding how your model’s predictions are impacting downstream business KPIs and revenue, it’s impossible to make further improvements and optimizations to your modeling pipeline. AI monitoring ensures that you are able to take preemptive actions before small modeling problems turn into catastrophic, system-level failures.

We’ve just passed the middle of March. For folks worldwide, this means gearing up for autumn or spring festivities and traditions, religious and cultural celebrations like St. Patrick’s Day, as well as more humorous events like Pi Day. For sports fans in the U.S., March is the unofficial month of basketball, and it’s when basketball gets a little crazy. Here’s the story of how sports and basketball connect with passion, madness, analytics, machine learning, and a billion dollars (or potentially at least a few millions).

As AI systems become increasingly ubiquitous in many industries, the need to monitor these systems rises. AI systems, much more than traditional software, are hypersensitive to changes in their data inputs. Consequently, a new class of AI monitoring solutions has risen at the data and functional level (rather than the infrastructure of application levels). These solutions aim to detect the unique issues that are common in AI systems, namely concept drifts, data drifts, biases, and more.

If you’ve been struggling to get some transparency into your AI models’ performance in production, you’re in good company. Monitoring complex systems is always a challenge. Monitoring complex AI systems, with their built-in opacity, is a radical challenge.