The Pragmatic Programmer for Machine Learning (2023)

by rramadass • 2 years ago

195 41 2 years ago

rramadass18• 2 years ago

I am quite surprised there is no discussion here. The book actually gives a nice overview of practical Software Engineering principles applied to ML Engineering and hence of use to regular Programmers moving to ML from other domains. I personally found it quite useful to understand the practices employed in ML Engineering and how it is different from "normal" programming which is where i come from.

Part II titled "Best Practices for Machine Learning Pipelines" and starting from chapter 5 is where the meat lies.

nerdponx• 2 years ago

It's new to me! It certainly looks good, or at least like something that could be increasingly useful to an increasingly large group of people. But it's a whole book and I have only a few minutes' break to check HN, so I can't evaluate it for quality until I've had a chance to read it (and I definitely will, because it looks useful to me). I assume, being new, few other people have experience with it to comment. And fortunately whenever actual math and code show up, the AI maximalist/doomer blabbermouths tend to stay away.

dijksterhuis4• 2 years ago

I’ve had a skim through bits. It look good and thorough for ML Engineer / Data Engineer stuff.

Definitely recommend checking it out yourself, even if it’s only to bookmark for sending onto others as a reference manual.

And fortunately whenever actual math and code show up, the AI maximalist/doomer blabbermouths tend to stay away.

When I see something mentioning machine learning + engineering, I’m interested.

When I see AI mentioned I automatically discount it as marketing fluff.

It’s worked well as a filter for almost 10 years now (the last hype cycle).

FrustratedMonky• 2 years ago

came here to ask.

Since I don't know enough about ML to know if this is good, is it as good as the original 'Pragmatic Programmer', to justify the title?

A lot of books rif off a poplar name, so asking if this lives up to it, and worth more time investment to read it.

rramadass18• 2 years ago

https://news.ycombinator.com/item?id=41563211

It is a overview of practical issues book and hence an easy read.

simonw• 2 years ago

Does this have anything to do with the original Pragmatic Programmer book or the https://pragprog.com/ publishing company?

If not I think the name should be reconsidered. It's a distraction from the content of the book itself if it's not actually related to that other text.

rramadass18• 2 years ago

The phrase "Pragmatic Programmer" is a common one to denote somebody focused on "pragmatic issues" in Software Development and can be used in that capacity wherever it is applicable.

This book deals with such practical issues in ML software engineering and hence very much worthy of this name.

sja• 2 years ago

I tend to agree with the parent comment. The name of the book sounds like it could be part of a series authored by the same people as “The Pragmatic Programmer”. For me, I subconsciously internalized the grammar of the title as “The Pragmatic Programmer: for ML”.

I have no idea what the expectations are legally, but given the original “pragmatic programmer” book has been out for around for ~25 years and is extremely well known, it seems like a reasonable name collision to avoid.

dcchambers• 2 years ago

Wow what's the context behind this? Is it worth reading? Who are the authors? Looks like this was first published in print in 2023 but there's basically no reviews online.

rramadass18• 2 years ago

https://news.ycombinator.com/item?id=41563220

Read the introduction to see for yourself.

vismit2000• 2 years ago

A good related book is 'Designing Machine Learning Systems' by Chip Huyen which covers similar topics: https://www.oreilly.com/library/view/designing-machine-learn...

rramadass18• 2 years ago

This looks pretty good and more aligned to what i was looking for;

Thanks for the pointer.

dijksterhuis4• 2 years ago

Had a skim, looks good. Bookmarked.

+1 adversarial robustness [0] & privacy were included in the analysis stage. People forget that stuff.

+1 on having to rewrite academic code (or code from some Jupyter notebook). Bane of my life sometimes.

+1 versioning data and code, running pipelines based on changes in either

+1 ingest your data, then validate, then use it. Data/model drift etc.

+1 on consistent tooling and language use.

+1 references everywhere

Wasn’t sure about the super specific approach to the commit history (squashing specific file changes together with validation/safety changes in a separate commit).

But then I’ve rebased my MRs to do something similar before and enjoy doing it. I guess I’m just pointing out that trying to get other people to do this regularly is a massive PITA and usually doomed to fail.

—

[0]: adv robustness was a bit light on content unfortunately. But then I researched to topic full time for three years so probably always gonna be light for me KEKW

rramadass18• 2 years ago

Finally! Somebody who is actually talking about the contents :-)

Could you clarify a little bit on what is meant by "Concept/Data Drift"? Any examples/links you can point us to? Wikipedia (https://en.wikipedia.org/wiki/Concept_drift) describes it but without a specific example to walk through i am not really "getting" it.

tomtom13372• 2 years ago

I haven't read the text, but data drift refers to how, after deploying a machine learning model, the input data changes over time to something that wasn't tested on. For instance, let's say you create a gradient boosting forecasting model that does a great job at predicting tomorrow's earnings. At the time of training, the earnings might be in the $1000 per day range. But a year later, the earnings might be in the $100k range. The model has never seen numbers this high before, so it doesn't know how to handle them well. That is data drift.

dijksterhuis4• 2 years ago

is mentioned in the text in a couple of places

https://ppml.dev/design-code.html#data-debt

https://ppml.dev/troubleshooting-code.html#troubleshooting-d...

Probably a very over simplified example below. Because data doesn’t usually drift in this obvious way. It’s usually more subtle and happening over a longer period.

My model is learning on my business data of orders over time.

People keep ordering every day. but usually in small amounts.

But today we got a new customer and they put in monthly orders which are 1000x larger than all other combined.

They are going to keep making orders for the next year or so. At which point they stop ordering from us.

Two data drift “episodes” here:

1. When we get the new customer. We’ve now got an outlier. They aren’t like all the other customers. How’s will the model react to this when being trained? Will it skew the output? Do we exclude the new customer in training data? Or do we change the model to account for them?

2. When that customer stops ordering after a year. Now the outlier is gone. But maybe we changed some model settings and tweaked it a bit to account for it. Now we need to account for that customer not being around anymore.

Data drift is a big PITA.

BodyCulture• 2 years ago

I can haz PDF, please…

Don’t eat me!

rramadass18• 2 years ago

Psst...I heard about this awesome invention called "Search Engines" ...

pmg101• 2 years ago

Even so, it is difficult to understate the impact that machine learning is having on many aspects of our lives.

I'm struggling to parse this. Does this mean that the impact has been so small that it is very difficult to understate it? Do they mean "it is difficult to OVERstate the impact", meaning that the impact has been large?

Jeff_Brown• 2 years ago

They definitely meant the latter.

The Pragmatic Programmer for Machine Learning (2023)

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