Adaptive Thresholding In ML-Based Alerting Systems: Minimizing False Positives In Production Environments

The field of machine learning and anomaly detection systems has become integral to the way organizations maintain operational stability, particularly in industries like e-commerce, finance, healthcare, and cybersecurity. As these sectors face increasingly complex challenges involving large-scale data and high-volume transactions, the need for accurate, real-time anomaly detection has never been greater. This is where adaptive thresholding, powered by advanced techniques like long short-term memory (LSTM) networks and auto-encoders, comes into play.

Hariprasad Sivaraman is a key figure in this arena, bringing a wealth of experience in developing and implementing machine learning-driven solutions that address the core challenges of traditional alerting systems. Through his research titled “Adaptive Thresholding in ML-Driven Alerting Systems for Reducing False Positives in Production Environments,” published in the International Journal of Scientific Research in Engineering and Management in 2024, he has pioneered improvements in anomaly detection by introducing dynamic, data-driven thresholding techniques that enhance the accuracy and relevance of alerts, reducing operational noise and alert fatigue. “The traditional static thresholds were no longer sufficient in today’s fast-paced production environments. The shift to adaptive systems using machine learning models has been transformative,” he reflects.

By implementing adaptation thresholds, he and his team supported the organization reduce the false warnings by 40%, leading to significant cost savings. In one case, this improvement alone amounted to around USD 250,000 per year for several teams. This helped engineers to focus on critical issues, increase efficiency and reduce warning times. By improving resource utilization, overtime costs were recorded as a 25% reduction.

By preventing alarm fatigue, the adaptive model played a key role in maintaining system monitoring during upper traffic hours. In terms of efficiency improvement, his approach to reducing clinical resolution time by 35% is because important anomalies were recognized and addressed more quickly. Additionally, engineer workloads were optimized, reducing the number of notifications by 50%, and had to be processed weekly. This shift helped teams to focus on high quality tasks and strategic improvements. Moral and productivity improvements were part of the good results,” he pointed out, adding that employees have grown by 20%, continuing to contribute to better cooperation and reliability of the system.

He has been involved in several projects, including the deployment of adaptive thresholding techniques for high-traffic e-commerce platforms, financial fraud detection systems, and cybersecurity alert optimization. Each project aimed to tailor machine learning models to the specific needs and complexities of the organization, ensuring that systems could handle the growing complexity of modern data environments.

The reduction in false positives, improved fraud detection, optimized system reliability, and enhanced operational efficiency has had clear financial and operational benefits. “When the systems we build not only work but also drive real savings and improvements, that is when the true impact becomes clear,” he emphasizes.

He faced complexities. “Alert fatigue had become a significant issue, and organizations were struggling to differentiate between critical and non-critical issues,” he recalls. By integrating adaptive thresholding with LSTM networks and auto-encoders, he was able to create a more reliable, efficient system. This change not only reduced false positives but also ensured that critical anomalies were flagged more accurately, leading to fewer missed or delayed responses.

He worked with teams to demonstrate the benefits of machine learning-driven approaches, using case studies and results to win stakeholders over. “The shift towards machine learning in operational systems requires a clear demonstration of its potential. It’s not just about adopting new tech, but about understanding its impact,” he concludes.

Hariprasad’s work combined with a focus on explainable AI and ethical considerations will shape the next wave of operational monitoring systems.