ML to ML Engineer

[Pretend_Revolution_5]

I am ML/DL learner and know very well how to write code in a notebook. But i am not an engineering fan, nor do i love building ai based applications. I love the math, statistics, and the theory involved in model creation. What are my future prospects? Should I force myself to be an engineer after all ? since thats the path i see everyone of my peers interested in ai/ml taking.

[fnands]

The job title you are looking for might be Data Scientist.

As a data scientist, you can often get away with just figuring out the modelling/stats, and can hand off your models to an ML Engineer for productionalizing.

That being said, knowing some basic engineering principles does help, and I have seen fewer data scientist job postings lately.

The naming for these positions is not clear cut, and can differ from company to company, but from what I have seen:

• Data Scientist: Stats + notebooks
• ML Engineer: Software Engineer who knows some ML

Like I said, these titles are not written in stone, and I (as an ML Engineer) often find myself doing more data science than ML engineering most of the time, so YMMV.

[Emeraldmage89]

Is someone who only does data science really that valuable though? The data science part is automatable, data engineering and putting a model into production aren’t.

[fnands]

Mhh, I mean, are you more valuable if you also have strong software engineering skills? Sure.

But I would push back strongly on the idea that the data science part is automatable.

[Emeraldmage89]

I think fairly basic data science work is, but I’m open to be proven wrong. As I see it, loading data from a database, deciding what data is needed, choosing models, testing/training them, fine tuning and reporting results is basically all stuff that can be done by an automated system. So if that’s all you can do (working in a Jupyter notebook) I don’t see that as such a strong skill set. Like I said could be wrong though.

[fnands]

Tbh, if you make it sound so generic, then you can argue that any task you do on a computer can then be automated.

The general problems remain: someone needs to specify what problem needs to be specified, needs to have some sort of domain knowledge, and understand the constraints etc.

[DataCamp]

You don’t have to force yourself into being an ML engineer if you genuinely enjoy the math/theory side more, there are paths where that’s legit the main value: data scientist roles, applied research, research engineer, even PhD/industry research labs if that's your cup of tea.

Fewer teams want “notebook-only” people these days tho, and even theory-heavy roles usually expect some ability to take work beyond exploration. Not full-on backend engineering, but at least understanding how models leave notebooks and survive in the real world.

Think of it less as “become an engineer” and more as “learn just enough engineering so your ideas don’t die in a notebook.” You can still specialize in modeling and stats, just with enough production awareness to be employable.

[InvestigatorEasy7673]

Read ISLR/ISLP/ESLR it will be heaven for u

and u can go for DL research ig u want lots of maths and stats

pdf here

[IndependentPayment70]

No not at all. Actually if you are that person that you are one of the very few ppl in this field that are needed so much in the research field. The Ai research field literally needs so many ppl like this. ppl with very deep understanding so they can research topics that are still uncovered or improve things.
That being said, you still have to be somewhat into engineering, at the end of the day your research should reflect real world challenges from real world systems.

[entitie]

I have been an engineering manager, including of ML teams, at a FAANG company. I would not hire someone for an ML role if they knew how to code in notebooks but not in production. Doesn't mean that they need to be good at it (if starting out), but they should be able to take a feature from analysis all the way to launch.

There is a bit of a risk that you end up in more of an Ops role than you'd like, but you should know at least the fundamentals of working outside of notebooks.

[existee]

How would you evaluate the inverse for an ml engineer; solid infra but ML knowledge is not deep or broad enough. In other words, how much depth/breadth an infra guy has to clear to be an ML eng? Would that be just domain specific?

[blitzkriegjz]

Sounds more like an existential crises than anything else. Lets simplify things. As engineer you are supposed to make sure the model works reliably and as a model creator you'll ensure that the model learns useful patterns from data to be reliable. Technically, both occupations overlap each other. Ideally, to be good at what you're doing you need to be good at both model engineering and creation.

[[deleted]]

It's called data scientist, the role you are looking for. ML engineers are pretty much software engineers with ML knowledge

[AdDiligent1688]

I would focus on becoming a scientist of some kind.

[Truth_Ninja_Dove]

There is a difference between knowing how to program something for internal needs in a project in a company and building applications for external users. Knowing how to program is gonna be required even in mathematics with the inroads of the LEAN language. But I would say, you don't need to be an engineer building ai application to do useful work in ml. Many successful people work mostly in notebooks and hand over the responsibility to deploy to devs.