Recent posts by Mona

Artificial intelligence (AI) and machine learning (ML) models drive mission-critical decisions in industries ranging from finance to healthcare. Without robust monitoring, these models can degrade over time, leading to poor performance, biased predictions, and even compliance risks. That’s why model performance monitoring is a critical component of AI infrastructure, ensuring both AI reliability and business impact.

But how do organizations evolve from basic monitoring to fully integrated, proactive AI observability? One way to think about it is through a four-stage maturity curve, which starts with simply collecting relevant data and ends with full monitoring operations. Understanding these stages can help organizations assess where they stand and what steps to take next.

In the world of automated trading, quantitative (quant) models are at the core of decision-making. These models analyze vast datasets to execute trades at speeds and volumes that far exceed human capability. However, ensuring that these models consistently perform well requires effective monitoring. Traditional approaches to performance management, which focus on broad financial metrics, IT infrastructure, and standard machine learning (ML) monitoring, are often insufficient. Specialized, deep monitoring is necessary to truly understand how these models behave and to maintain their effectiveness over time.

The Challenges of Algorithmic Trading

Automated quantitative trading systems are sophisticated, but they face a myriad of challenges that can disrupt their performance. From issues with data and models to internal operations, there’s a lot that can go wrong.

Monitoring is critical to the success of machine learning models deployed in production systems. Because ML models are not static pieces of code but, rather, dynamic predictors which depend on data, hyperparameters, evaluation metrics, and many other variables, it is vital to have insight into the training, validation, deployment, and inference processes in order to prevent model drift and predictive stasis, and a host of additional issues. However, not all monitoring solutions are created equal. In this post, I highlight three must-haves for machine learning monitoring, which hopefully serve you well whether you are deciding to build or buy a solution.