Just recently we published an important update on our growth, from recent customers to our team growth. Today, I’d like to go a little deeper on our current product and share how we’ve been expanding it in multiple areas to create value for our customers.
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).
Making AI impactful and scalable is hard
In virtually every industry, companies invest heavily in AI. We all have an intuitive understanding of the “why”: Within just...
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.