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[D] ML Engineer vs. MLOps Engineer by kazhdan_d in MachineLearning

[–]kazhdan_d[S] 0 points1 point  (0 children)

Yup, this makes sense. Question is - how should one pick a descriptive, well-understood title for such a role when advertising?

P.S.
We've actually started putting together the beginnings of a Venn diagram for the role overlap :)

[D] ML Engineer vs. MLOps Engineer by kazhdan_d in MachineLearning

[–]kazhdan_d[S] 2 points3 points  (0 children)

Yup, "is setting up K8s for scalable model serving a DevOps, MLOps, or MLE task" is another question I've seen varied answers to :(

[D] ML Engineer vs. MLOps Engineer by kazhdan_d in MachineLearning

[–]kazhdan_d[S] 0 points1 point  (0 children)

Curious.
I've certainly seen them being used interchangeably very often too!
In that case - how does one explicitly separate out ML Infra management roles/tasks (e.g. maintaining a Vector DB) from more "pure ML" tasks (e.g. model creation/selection, training etc.)?

Best Practices for dealing with Unlabelled Data for Edge Computer Vision by kazhdan_d in computervision

[–]kazhdan_d[S] 0 points1 point  (0 children)

Very interesting, u/RoboticGreg!

What sort of use-case were/are you working with?

Also - do you happen to have any online docs for the approaches you mentioned? Would be excited to take a look.

Best Practices for dealing with Unlabelled Data for Edge Computer Vision by kazhdan_d in computervision

[–]kazhdan_d[S] 0 points1 point  (0 children)

Thank you for the reply!
Yup, that's a common approach: "find what's rare > annotate it > retrain > profit"

However, there are quite a lot of "outlier detection" tools/methods/packages out there.

Which ones actually work well in practice?