Thoughts on ML Engineering After a Year of my PhD
Read OriginalThe article details the author's reflections after a year of PhD research on Machine Learning Engineering (MLE). It distinguishes between 'Task MLEs,' who manage specific production ML pipelines and face operational burdens, and 'Platform MLEs,' who build underlying infrastructure. The author shares hard-won lessons on automation, monitoring, retraining strategies, and the practical, often unrigorous, realities of maintaining business-critical ML systems.
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