[HN Gopher] Machine Learning Models Are Missing Contracts
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Machine Learning Models Are Missing Contracts
Author : Aliabid94
Score : 17 points
Date : 2021-01-19 18:05 UTC (4 hours ago)
(HTM) web link (gradio.app)
(TXT) w3m dump (gradio.app)
| gillesjacobs wrote:
| I cannot agree with OP more. ML model code itself is too often
| seen as documentation for a paper, in the sense that authors
| implicitly expect users to go through the pre-processing pipeline
| to find the actual implementation steps.
|
| This is because the data handling and pre-processing nitty gritty
| is not actually interesting from an academic perspective.
|
| I cannot count the times I went to look at a paper's
| implementation source code on a benchmark dataset to find it cut-
| off annotated sequences to a fixed max length, essentially
| turning it into a different dataset making comparison to previous
| work invalid.
|
| Good documentation costs time, time academics don't have.
| aliabd wrote:
| I think ML as a field just needs to really mature. A lot of the
| work feels super hacky, sort of a mix between research, demos,
| and production.
| fuzzybear3965 wrote:
| I don't think the author would disagree with you. In fact, I
| think this article was highlighting one specific area in which
| the field could improve.
| mlthoughts2018 wrote:
| More specifically, at least in industry, we need SRE / ops
| support to mature. Taking a team of people who are highly
| specialized at the research layer of a statistical computing
| problem, then treating them like they are immature when they
| get massively overloaded also solving credential management,
| Kubernetes config, web service hardening, efficient data
| pipelining, etc. etc. is just such a whiny and immature thing
| to see come out of infra / ops team leaders, that leads to
| burning out ML engineers, and wasting a lot of money failing to
| extract value from their comparative advantage for the business
| just because infra / ops leaders can't get it together and
| solve ML coordination problems.
| Guest42 wrote:
| Right. I think it ignores the importance of the data when
| building a model. Even great data can lead to difficult
| modeling scenarios. The premise that a "solution" can guarantee
| (or even partially guarantee) to be useful is misleading.
| data_ders wrote:
| What's the difference between a test and a contract? I agree that
| code in the ML space needs to be more rigorously tested
| especially the data flowing in and out. But how are contracts
| different?
| drewcoo wrote:
| A contract defines how the parties should interact. A test
| determines how something is or behaves. Contract tests are a
| thing.
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