Nicholas Beaudoin works at the intersection of machine learning and strategic decision-making in public and commercial organizations. As a data science manager with large and small consulting firms, including Deloitte, Capgemini, Eviden, and Washington, DC, analytic/policy think tanks, he developed AI/ML solutions, methods, data pipelines, and data visualization. His experience comes from helping companies ideate, build, and deploy machine learning solutions, including the infrastructure to support them. He has helped numerous Fortune 500 companies such as Mercedez-Benz, Honda, Warner Brothers, Disney, Estée Lauder, national insurance providers, and various federal government departments ranging from the Department of Defense to the Department of State and Department of Agriculture.
Mr. Beaudoin’s expertise resides in the following domains:
- End-to-end machine learning lifecycle
- Machine learning deployment strategies
- Cloud-based ML integrations on AWS, GCP, and Azure
- DevOps best practices for machine learning
- Machine learning operations (MLOps) orchestration and management
- Applied generative AI (large language models - LLMs)
- Machine learning open-source toolkit
Mr. Beaudoin is an instructor for Caltech CTME’s programs, where he teaches machine learning, machine learning operations (MLOps), generative AI, and cloud-based machine learning.
Mr. Beaudoin holds a Master’s degree in International Affairs, with a focus on International Economics and Econometric Modelling, from UC San Diego and a Bachelor’s degree in Political Science from Lewis & Clark College. In addition, he holds numerous advanced certifications in AWS and Google Cloud services.