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Fig 4 looks like it's derived from Hidden Technical Debt in Machine Learning (2015). https://papers.nips.cc/paper/5656-hidden-technical-debt-in-machine-learnin

Fig 4 looks like it's derived from Hidden Technical Debt in Machine Learning (2015). https://papers.nips.cc/paper/5656-hidden-technical-debt-in-machine-learning-systems.pdf https://papers.nips.cc/paper/5656-hidden-technical-debt-in-m... As someone else says in this comment thread, this is very much an organizational problem, and cannot be viewed as just a technology problem. The common behavior of individuals and teams is the pursuit of solutions that solve problems for them. Problems here with ML, and as we've seen with "Data Science," along with other magic technologies is that having an appreciation for the domain or context goes a long way. Being familiar with entire process, or "pipeline," is valuable, and role/functional silos often lead the problems people experience. For some classes of machine learning problems and associated data, sourcing solutions from vendors can work, but as with any tools you can procure, you need the right people to use them appropriately. This also applies to "DevOps" which is used for comparison in the blog post. --> DevOps example -- the philosophy seems to be about having software developers also share build/release and infrastructure responsibility. But some organizations have made "DevOps" teams to silo build/release and infrastructure work... they ended up renaming what used to be called their Build/Release or SysAdmin teams. Siloing things to be "someone else's" problem doesn't result in the major transformations that are needed. Now imagine what happens if we substitute MLDevOps for DevOps above. I'll continue to say "The Role of a Data Engineer on a Team is Complementary and Defined By The Tasks That Others Don’t (Want To) Do (Well)"

amznthrowaway5 6y ago
> The Role of a Data Engineer on a Team is Complementary and Defined By The Tasks That Others Don’t (Want To) Do (Well) Those types of tasks are also often not recognized or rewarded by management, despite being a hugely critical part of the system. I believe the incorrect hiring of scientists who are often strong in terms of core theory or number of papers published but have no clue about building real production ML systems is a huge organizational problem, often causing ML teams to fail to deliver any real value.

amznthrowaway5 6y ago

> The Role of a Data Engineer on a Team is Complementary and Defined By The Tasks That Others Don’t (Want To) Do (Well) Those types of tasks are also often not recognized or rewarded by management, despite being a hugely critical part of the system. I believe the incorrect hiring of scientists who are often strong in terms of core theory or number of papers published but have no clue about building real production ML systems is a huge organizational problem, often causing ML teams to fail to deliver any real value.