It’s hard to believe that it has been almost three months since we turned the page on 2020, filling our hearts with new hope for a better year to come. I am proud to share with you that Mona has come out strong from an unexpected and challenging year. As we take a moment to catch our breath and reflect on our recent accomplishments, I wanted to take a moment and share some updates with you.

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).

Looking for a special gift for a data scientist or a data engineer at the office or in your life? You’ve come to the right place then. It’s just so hard to please them! We spent hours crunching data and running experiments with various gift ideas, narrowing the list to some of the best options so you won’t have to. Happy shopping!

AI teams across verticals vehemently agree that their data and models must be monitored in production. Yet, many teams struggle to define exactly what to monitor. Specifically, what data to collect “at inference time”, what metrics to track and how to analyze these metrics. 

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 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, biases, and more.