ISyE Seminar Series: Kyle Stahl
"Machine Learning Operations"
Senior Data Science Supervisor
About the seminar:
What happens after you build a machine learning model? A recent study from McKinsey showed that 85% of machine learning projects fail. The hardest part of machine learning is not building an accurate model, it is integrating that model into a software system so people can get value from its results. This presentation offers a detailed introduction to MLOps, an evolving domain blending machine learning with software development. It focuses on strategies for deploying, continuously monitoring, and periodically updating ML models, ensuring their optimal performance and relevance in dynamic real-world applications.
Kyle Stahl is a senior data science supervisor at Cargill Inc, where he leads a team of data science specialists in solving complex machine learning and optimization problems across the agriculture industry. He has applied advanced analytics techniques to various industry groups within Cargill, including global supply chain, animal growth modeling, biochemistry, sales, agronomy, and production scheduling. He also teaches the Data Science II class in the Information Technology Infrastructure program in the College of Continuing Education and Professional Studies.