Eric Riddoch is an ML platform engineer and course creator who helps data science teams double or triple their productivity by making MLOps practical and approachable. With eight years of experience building ML platforms, infrastructure-as-code patterns, and self-serve tooling, he has led platform teams at high-scale e-commerce and AI companies to enable dozens of data science use cases. He focuses on pragmatic simplicity—creating constructs, boilerplate, and CI/CD that let statisticians and analysts ship without becoming DevOps experts. Eric is active in the community as a mentor and open-source builder (rootski, awscdk-minecraft, and projen POCs) and runs a micro-degree to bridge coders into MLOps engineers. He combines hands-on engineering (IaC, model serving, testing in prod) with team leadership and evangelism, and he’s unusually focused on making “testing in production” safe and routine for ML systems. Based in Salt Lake City, he balances product-minded platform work with creating educational content that scales his impact.
8 years of coding experience
6 years of employment as a software developer
Bachelor of Science - BS, Applied Mathematics, Bachelor of Science - BS, Applied Mathematics at Brigham Young University
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