David & Elle talked about how one of the staples of DevOps, the practice of continuous integration, can work for machine learning. Continuous integration is a tried-and-true method for speeding up development cycles and rapidly releasing software- an area where data science and ML could use some help. Making continuous integration work for ML has been challenging in the past, and we chat about new open-source tools and approaches in the Git ecosystem for levelling up development processes with big models and datasets.
In this episode
Lecturer and Research Investigator, University of Michigan
Researcher and advocate specializing in research methods and MLOps. Skilled in applied and pure mathematics including machine learning and deep learning with experience in advanced statistical modeling, natural language processing, and signal processing.
David is one of the organizers of the MLOps Community. He is an engineer, teacher, and lifelong student. He loves to build solutions to tough problems and share his learnings with others. He works out of NYC and loves to hike and box for fun. He enjoys meeting new people so feel free to reach out to him!