At Mona, we strive to enable better visibility into AI systems in order to reduce the associated risk with production AI, optimize the operational processes around versions and releases, and plan better AI roadmaps using feedback based on production data.
As our customer base grows and the number of production AI use-cases being monitored by Mona increases, our team has been working tirelessly to advance our product to become a best in class AI observability solution.
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.
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.