Before you launch a project to build an AI monitoring system from scratch, consider whether or not this would be a good use of your resources. When does it make sense to buy instead? Let’s discuss. This post explores the advantages and disadvantages of both alternatives so that you can make an informed decision about what’s best for your organization.
Posts by Yotam Oren, Co-founder and CEO:
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.
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.
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.
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.
Below, we use the term AI system. By this, we mean any software system that incorporates at least one predictive model, leveraging machine learning, statistical modeling, or any other AI techniques. A few examples: An automatic fraud detection system, a recommendation system, an image classification system, and a sentiment analysis of social media posts.