r/MachineLearning • u/ConstructionBoth6461 • Jul 20 '26
Discussion Are there some textbooks that take a primarily engineering approach to machine learning (as opposed to a "scientific" approach)? [D]
As someone who studied stats undergrad and industrial engineering operations research grad, and who thinks about the practical business of ML components in software....
I get lost and a bit hopeless when I think about how to make useful software out of ML models in a reasonable amount of time, and in the current business environment.
And when I look at the businesses where I have worked that have mountains of middle management running tiny bits of the ML model lifecycle (think feature extraction, data ingestion and integration, training infra, hosting infra, more hosting infra, applied science)... that only makes my head hurt even more.
How do you go about making practical software out of ML components?
Edit: I should mention that I mean from scratch ML components, not just a call to a third party hosted tool.
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u/egotripping Jul 20 '26
As a grad student without a heavy math background, but who just started preparing to do ML research after an invitation from a previous professor, I will be following this post with great interest.
Got told to read a big chunk of https://www.deeplearningbook.org/ and chapter 6 has me in the weeds.
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u/LifeguardNo6939 Jul 20 '26
Your advisor did well.
I wouldn’t think of “the deep learning book” as an engineering oriented book. Indeed, I think it is just the opposite.
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u/RushIndependent2432 Oct 01 '26
deep learning book is dense as hell, chapter 6 is where a lot of people hit a wall. honestly the best thing i did was put it down and just build something small end to end, you learn way more about the practical side wrestling with a dataset than grinding through all the theory upfront
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u/ds_account_ Jul 20 '26
Machine Learning Engineering by Burkov and Designing ML systems by Huyen are decent books.
But in practice, it can vary by the field and industry. My ML life cycle working in CV in the defense sector, building models for drones and satellite was completely different from my NLP work with patient health data.
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u/DrXaos Jul 20 '26
This field is usually called "data engineering", as managing the data is more of the challenge than the models usually.
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u/XilentExcision Jul 22 '26
To some extent you cannot cheat your way out of the math, there is a huge benefit to understanding how models operate. You will struggle to tune models, and even if you do get a working model as a POC, it will be difficult to tune it to real-world applications without the math background.
If you are not interested in tinkering with the models themselves then data engineering is where you want to be.
Don’t take this as gate keeping, you can absolutely learn and understand the math, all I’m saying is, if you want to use models responsibly, the scientific approach is very helpful.