Designing Machine Learning Systems Quotes

“conferences”
― Chip Huyen, Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications

“There are many possible solutions to any given problem.”
― Chip Huyen, Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications

“most businesses don’t care about ML metrics unless they can move business metrics. Therefore, if an ML system is built for a business, it must be motivated by business objectives, which need to be translated into ML objectives to guide the development of ML models.”
― Chip Huyen, Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications

“Many companies create their own metrics to map business metrics to ML metrics. For example, Netflix measures the performance of their recommender system using take-rate: the number of quality plays divided by the number of recommendations a user sees.4 The higher the take-rate, the better the recommender system. Netflix also put a recommender system’s take-rate in the context of their other business metrics like total streaming hours and subscription cancellation rate. They found that a higher take-rate also results in higher total streaming hours and lower subscription cancellation rates.5”
― Chip Huyen, Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications