[D] What are your top 3 pain points as an ML developer in 2023?

Submitted by General-Wing-785 t3_126zxjo in MachineLearning

As an ML developer, what are the top 3 pain points that you would love to see solved. This could be something very specific (Example: I spend a lot of time in building a model architecture using Keras) or something broad (Example: I struggle with Model Serving because of the lack of easy to use infrastructure). Also, please mention how you workaround these paint points. Looking forward to the discussion. Thank you!

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cthorrez t1_jecv5ox wrote

Code quality of my own and my team's code

The reliability of the engineering platforms we use. (spark, gpu clusters, ci/cd build pipelines)

The correctness and completeness of the data we ingest

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Philpax t1_jed8vd0 wrote

Deploying anything developed with Python to a end-user's machine

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General-Wing-785 OP t1_jedkzcg wrote

Replying to cthorrez (#2,511,623)

Thanks! A few follow up questions:

  • do you do most of your experiments in a notebook and then manually translate it to a classic python project for code review/deployments?
  • what has been the most frustrating part about the engineering platforms? Non deterministic build/compute times?
  • do you use any data auditing libraries for data quality?
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Philpax t1_jee04jo wrote

Replying to General-Wing-785 (#2,518,575)

It's just difficult to wrangle all of the dependencies; I want to be able to wrap an entire model in a complete isolated black box that I can call into with a C API or similar.

That is, I'd like something like https://github.com/ggerganov/llama.cpp/blob/master/llama.h without having to rewrite the entire model.

For my use cases, native would be good, but web would be a nice to have. (With enough magic, a native solution could be potentially compiled to WebAssembly?)

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