Using Graphs for Large Feature Engineering Pipelines


Graph data structures provide a versatile and extensible data structure to represent arbitrary data. Data entities and their associated relations fit nicely into graph data structures. We will discuss GraphReduce, an abstraction layer for computing features over large graphs of data entities. This talk will outline the complexity of feature engineering from raw entity-level data, the reduction in complexity that comes with composable compute graphs, and an example of the working solution. We will also discuss a case study of the impact on a logistics & supply chain machine learning problem. If you work on large scale MLOps projects, this talk may be of interest.


Wes is a machine learning expert with over a decade of experience delivering business value with AI. Wes's experience spans multiple industries, but always with an MLOps focus. His recent areas of focus and interest are graphs, distributed computing, and scalable feature engineering pipelines.

Open Data Science




Open Data Science
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Cambridge, MA 02142

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