What's the difference between a Machine learning Engineer and a Data Engineer in the industry?
Like many titles these days, they should be differentiated, but in practice can be muddied. MLE: building production ready ML models DE: building production ready data pipelines Flow: DE builds data pipelines to get data ready, DS builds initial model, DE or MLE ensures model has reliable access to features at production scale, MLE hardens the model for production deployment
Thanks for the detailed response. Can you shed more light on the "MLE hardens the model" part? Like parralelising the model created by DS? And does MLE involve in creating rest API for the ML models created?
MLE would harden models by using better engineering practices than DS who often have more prototype style code as opposed to production ready code. DS often even train models on small sample datasets and take shortcuts to make sure the model is viable, so the MLE would then have to ensure distributed training, testing and validation in addition to regular model/code reinforcement.
Generally a data engineer is responsible for data movement pipelines, storage and infrastructure. A data analyst will write code to process the data and create reports to find trends, business opportunity, anomalies, etc. An ML engineer will write code to train and test ml models which solves some automation problem.
SQL
Anyone else think Maximum Likelihood Estimation vs Distribution Entropy when seeing the title???
Lol may be their Brians are not so deep to get that thought!
I do, for MLE for sure, not DE though
AFAIK ML engineers are too specialized .Data engineers deal with all things data:MLsystems,analytics ,real time steaming systems ,real time analytics dashboards ,data platforms etc. I have worked primarily with python,spark,sql and ruby. The main difference I see between an SDE and a DE is that SDEs build services which DEs do not do.
Too specialized for what?
MLE is software engineers specialized in ML algorithms and systems. They are the most sought after SWEs. Data Engineer is very different, they have nothing to do with algorithm and machine learning, mostly using big data tools to build data pipelines for applications.
So many well explained answers.
Is there a difference?
Yes, that's why they have different naming I guess. I want to know the real difference if there is any.