In many organizations, data scientists develop machine learning models and data/ML engineers put them into production. The chasm between the two roles leads to many difficulties in moving models from development into production. These difficulties make it extremely difficult to maintain and enhance those models, a key requirement if ML models are used to drive the business. We describe four key concepts to keep in mind as you develop and operationalize machine learning models, and present a number of solutions (“patterns”) to realize these four concepts in practice.
Original talk by Edmond Chan, Yufeng Guo, and Valliappa Lakshmanan
Rewind by Yufeng Guo
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