> [!META]- Inline Metadata > [tags:: #note/literature #state/to-process #concepts/programming/machine-learning/ml-engineering ] > [status:: to process] ## Note Task MLEs work with data scientists to "productionize" models, sustaining a specific pipeline or pipelines in production. They need to write, maintain, and monitor pipelines that are responsible for the entire lifecycle of a ML model. Platform MLEs help task MLEs automate their job. They build pipelines that support multiple tasks (task MLEs solf specific tasks). Platform MLEs build pipelines to create features, task MLEs create pipelines to use features. PMLEs trigger ML performance drop alerts, TMLEs act on alerts. ### My Thoughts My experience is largely doing both. This is probably an artifact of how small my teams have been. ## Quote(s) ![[Thoughts on ML Engineering After a Year of my PhD#^afx5if]] ![[Thoughts on ML Engineering After a Year of my PhD#^x56puj]] ![[Thoughts on ML Engineering After a Year of my PhD#^gugp4a]] ![[Thoughts on ML Engineering After a Year of my PhD#^7m4ksf]] ![[Thoughts on ML Engineering After a Year of my PhD#^qcbqfv]] ## Source [[Thoughts on ML Engineering After a Year of my PhD]]