Recent posts by Mona

Large language models (LLMs) are rapidly becoming the foundation of modern NLP applications — powering everything from chatbots to personalized recommendations. But with great power comes greater complexity.

Integrating LLMs into real-world products introduces new risks: privacy violations, prompt injection attacks, hallucinations, and uncontrolled costs. These aren’t just technical quirks — they’re business-critical issues that can damage user trust, break regulatory compliance, or spiral expenses out of control.

So, the question is no longer “Can we use an LLM?” but rather, “Are we ready to deploy one to the public — safely, responsibly, and at scale?”

In this post, we’ll explore the key risks that come with production LLM usage and why monitoring is the essential tool for ensuring your LLM application is truly public-ready.

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.

In recent years, the term MLOps has become a buzzword in the world of AI, often discussed in the context of tools and technology. However, while much attention is given to the technical aspects of MLOps, what's often overlooked is the importance of the operations. There is often a lack of discussion around the operations needed for machine learning (ML) in production, and monitoring specifically. Things like accountability for AI performance, timely alerts for relevant stakeholders, the establishment of necessary processes to resolve issues, are often disregarded for discussions about specific tools and tech stacks. 

In today's data-driven world, organizations increasingly rely on data to inform their decision-making, resulting in the need for efficient and accurate data analysis tools. In the last two decades, a plethora of tools for analytics, data science, and BI have been created to meet this need. However, one basic problem in data analysis has remained elusive: the problem of automating multivariate exploratory analysis clearly and free of noise.