Tyler Beauregard
About Tyler Beauregard
Tyler Beauregard is a Machine Learning Engineer with extensive experience in developing and deploying machine learning systems. He currently works at Corvus Insurance and has previously held positions at PeoplesBank, BlueHalo, and the University of Massachusetts Amherst.
Work at Corvus Insurance
Tyler Beauregard currently serves as a Machine Learning Engineer at Corvus Insurance, a position he has held since 2021. In this role, he specializes in container-based infrastructure-as-code using Terraform, which supports the productionalization of machine learning systems. He has established CI/CD practices for the Data Science and Machine Learning teams using GitHub Actions, significantly enhancing the efficiency of model development and deployment processes. Additionally, he designed and implemented an Automated Cloud Training Pipeline that incorporates automated Sagemaker training jobs and a model registry for cataloging training data and model metadata.
Previous Experience in Machine Learning
Before joining Corvus Insurance, Tyler worked as a Software/Machine Learning Engineer at BlueHalo from 2020 to 2021. His experience also includes an Information Technology Summer Internship at PeoplesBank from 2019 to 2020, where he gained valuable insights into IT operations. Earlier in his career, he served as a Computer Science Intern at the University of Massachusetts Amherst from 2014 to 2015, where he developed foundational skills in computer science and engineering.
Education and Expertise
Tyler Beauregard holds a Bachelor's degree in Computer Engineering from the University of Hartford, where he studied from 2016 to 2020. He is currently pursuing a Master of Science in Artificial Intelligence at Johns Hopkins Whiting School of Engineering, a program he began in 2020. His educational background provides him with a strong foundation in both theoretical and practical aspects of machine learning and software engineering.
Achievements in Machine Learning Projects
Tyler has led significant projects that demonstrate his expertise in machine learning systems. Notably, he successfully separated a 3+ year old legacy system from existing production ML systems, achieving zero production failures and enhancing system monitoring capabilities. His work in designing and implementing automated training pipelines further underscores his contributions to improving operational efficiencies in machine learning workflows.