Exploiting the business value of data science doesnโ€t end with training a Machine Learning model โ€” in fact that is just the beginning. Data scientists want to maximize model performance while application developers want a deployment that builds repeatably and behaves predictably. Model serving smooths the transition from data science to applications in production. This talk will explain what model serving is, who should care, and show participants how to use the open source model-serving project, Seldon Core, to serve models on Kubernetes.
This session is told from a data scientist's point of view and documents building a model serving pipeline as a whole. This includes pain points of model serving such as clunky pipelines, but also celebrates the parts that work well, such as scalability within Kubernetes.
The audience will learn: the basics of model serving, why this is a relevant issue, how model serving offers relief for the data scientist/software engineer handoff, and know how to deploy their machine learning model with Seldon Core.
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