The challenges of specificity in monitoring AI
Monitoring is often billed by SaaS companies as a general solution that can be commoditized and distributed en-masse to any end user. At Mona, our experience has been far different. Working with AI and ML customers across a variety of industries, and with all different types of data, we have come to understand that specificity is at the core of competent monitoring. Business leaders inherently understand this. One of the most common concerns we find voiced by potential customers is that there’s no way a general monitoring platform will work for their specific use-case. This is what often spurs organizations to attempt to build monitoring solutions on their own; an undertaking they usually later regret. Yet, their concerns are valid, as monitoring is quite sensitive to the intricacies of specific use cases. True monitoring goes far beyond generic concepts such as “drift detection,” and the real challenge lies in developing a monitoring plan that fits an organization’s specific use cases, environment, and goals. Here are just a few of our experiences in bringing monitoring down to the level of the highly specific for our customers.