Recent posts by Mona

We hope that everyone had a fantastic holiday season and is now ready to tackle the 2022 New Year! Looking back to where we started in 2018 to where we are now, we have grown so much overall as a company. From three (⅓ balding) guys with a crazy idea nobody understood, through assembling a team of passionate trailblazers, and to building advanced features for Mona’s platform - now leveraged by incredible AI/ML teams at industry leaders, and even recognized by Gartner, we are continuing to strengthen our position as the leading monitoring solution for AI,  - providing the most flexible and comprehensive insight engine.

In the past 4 years I’ve been working with teams implementing automated workflows using ML, NLP, RPA, and many other techniques, for a myriad of business functions ranging from fraud detection, audio transcription all the way to satellite imagery classification. At various points in time, all of these teams realized that alongside the benefits of automation they have also added additional risk. They have lost their “eyes and ears on the field”, the natural oversight you get by having humans in the process.

Last week, we caught up with Michael Tambe, Head of Data Science of Amazon Advertising Field Sales, to discuss his views on some of the challenges and opportunities that data scientists face working with real world AI / ML solutions. Michael has been a data science leader for over 8 years, focused on building data science teams in go-to-market areas within sales, marketing, and pricing. Here’s the advice that Michael has for other business leaders looking to deploy AI solutions and the best practices on optimizing ML model performance. 

Deploying AI instantly brought value and growth to many businesses. However, it is well established that sustaining the value over time, not to mention maximizing it, could be quite challenging. Continuous optimization is the key to successful AI deployments. Beginning with a product that’s good enough, learning from how it performs in the real world, especially as the world (read: the data environment) changes, and then improving; then learning and improving again and so on. It’s a bit of an obvious insight but it is rare for AI-driven products to be perfect from day one.