Denis Gaitan
Denis Gaitan is an accomplished IT Specialist with a history in the software industry since 2012. Over the past five years, he has specialized in the field of data science, leveraging his extensive software expertise and craftsmanship. Two years ago, he joined Rabobank as a Software Engineer, where he continually broadens his knowledge by addressing challenges from diverse perspectives. As a problem-solver, he consistently goes above and beyond for clients, excelling in new environments through his adaptability and flexibility.
Session
In today’s financial sector, the continuous accuracy and reliability of machine learning models are crucial for operational efficiency and effective risk management. With the rise of MLOps (Machine Learning Operations), automating monitoring mechanisms has become essential to ensure model performance and compliance with regulations. This presentation introduces a method for continuous monitoring of model drift, highlighting the benefits of automation within the MLOps framework. This topic is particularly interesting because it addresses a common challenge in maintaining model performance over time and demonstrates a practical solution that has been successfully implemented in the bank.
This talk is aimed at data scientists, machine learning engineers, and MLOps practitioners who are interested in automating the monitoring of machine learning models. Attendees will be guided on how to continuous monitor model drift within the MLOps framework. They will understand the benefits of automation in this context, and gain insights into MLOps best practices. A basic understanding of MLOps principles, and statistical techniques for model evaluation will be helpful but not strictly needed.
The presentation will be an informative talk with a focus on the design and implementation. It will include some mathematical concepts but will primarily be demonstrating real-world applications and best practices. At the end we encourage you to actively monitor model drift and automate your monitoring processes to enhance model accuracy, scalability, and compliance in your organizations.