We’re living in unprecedented times wherein a matter of a few weeks, things changed dramatically for many humans and businesses across the globe. With COVID-19 spreading its wings across the globe and taking human lives we are seeing record jumps in unemployment and small business bankruptcies.

So, how does this downturn impact a_ business that is using AI_?

Today, AI is increasingly being applied by companies across industries, but AI is not the easiest technology to operationalize. Most production AI systems are patchworks of proprietary, open-source, and cloud-based technology amassed organically over time. However, the past few years have seen the emergence of GUI-based AI tools and open-source libraries to help enterprises less inclined to build in-house, successfully train, and deploy AI models.

**As these tools have surfaced, companies have come to realize that training and deploying AI is only the first step- **they must then monitor and manage their deployed models to ensure risk-free and reliable business outcomes. With the rise of higher-performing black-box models, the need to govern these models has become both more necessary and more challenging. Increasingly, companies are learning that:

“Training and deploying ML models is relatively fast and cheap, but maintaining, monitoring, and governing them over time is difficult and expensive.”

Indeed, because their performance can degrade over time due to changes in input data post-deployment, models require continuous monitoring to ensure their fidelity while in production. And while many existing monitoring technologies provide real-time issue visibility, they are often insufficient to identify the root cause of issues within complex AI systems.

#monitoring #explainable-ai #mlops #ai