Composition in Machine Learning: in Models, Tools, and Teams


Composition is an extremely fundamental idea at the heart of modern mathematics, but has far-reaching implications for Machine Learning practitioners and leaders. When building Machine Learning models, composition allows the developer to separate concerns, measurements, and data structures into the most essential components. When building Machine Learning pipelines, composition allows the developer to establish clear handoffs between services and tasks; compositional tools allow one to observe and orchestrate this process. When building Machine Learning teams, composition allows developers to hold ownership and expertise, while encouraging collaboration and inter-operability. In this talk, I will discuss the link between higher Mathematics, Micro-service infrastructure, Machine Learning models, and MLOps.


Dr. Bryan Bischof is the Head of Data Science at Weights and Biases, and adjunct professor of Data Science at Rutgers University. He’s previously worked in Time Series Signal Processing at Scale, Demand Forecasting, Global Optimization and Logistics, and Personalized Recommendations. He’s obsessed with math, and has a dog named Ravioli.

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