I've been slowly hacking away at a math, coding and ML knowledgebase in concert with efforts to increase my work endurance, with the intention of accruing hard, demonstrable skills that could serve me well in any capacity. I assumed this was the most robust use of my limited energy because these skills could be used in almost any white collar position which uses computers to perform repetitive tasks or intersects with structured and unstructured data.
At least, that was the idea x years ago. Now it seems we're on the verge of a centralizing and commoditizing revolution in ML and UX where entire swaths of knowledge producer skills will become obsolete.
Where will that leave people trying for either ML engineer or more data analysis focused DS roles? Or even just everyday utility scripting/automating powers? Can even the latter two can be largely replaced by hybrid finetuned multi modal language/code models. I feel a bit lost and like I have wasted my time.
Will only elite scientists in the top percentiles of skill and resources be needed or will the future have room for more pedestrian aspirants?
A lot of the time you could train model prepare pipeline and test enviroment for 3-6 months before you get good enough result to push it into production. And it can get extremely stressful rly fast if you care about that, because not every model is good enough for production so once per year you could have as low as 1 or even 0 models that are working fast and good enough for proper usage and this can burn you after just a 1-2 years of work in the field (I know at least 4 people who just drop ML and go for SWE after 1+ year of ML work and all of them are a lot happier with classical backend/devops jobs).
In SWE after 2-3 days you can have small stuff working fine and after few weeks push your small code into production codebase fixing some stuff or optimising smth as there is a ton of potential in almost every codebase for "easy" upgrades in ML space everything is extremely competitive your results are "state of the art" or they are not if you want to upgrade model it better be sota or you would get asked "why we don't just implement/use ...?".
I still love my job but for sure but I prefer working with ops, classical SWE and deployment then model training, optimising and learning/collecting new datasets (I have 5 years of commercial exp in ML/DL maybe it gets better after 10 years or I'm just boring out who knows)
> Now it seems we're on the verge of a centralizing and commoditizing revolution in ML and UX where entire swaths of knowledge producer skills will become obsolete.
this is kind of vague and uncertain, I certainly wouldn't plan my career around it. It's not a good enough reason to change career paths IMO.
If you aren’t one of those, being a regular swe is much better. A lot of ml roles are using canned models, and increasingly so. Many data science insights are ignored by the business, or the statistics don’t work on such a biased sample.
What I'd say is since everyone is shedding employees that the next 6 months - year or longer, however long the recession is bad lasts, will be bad for all tech people. But as the economy improves and the recession recedes, good companies are going to want to know where to spend money to capture that growing market.
IME most people have a bit of culture shock when they first get an ML/DS role. When you learn ML/DS, there's a huge focus on the coding and mathematical parts of the job, and not on much else. When you get a job, suddenly you're exposed to everything else and you realize why every data scientist says that ML is only a tiny part of their job.
There's a lot of variety in DS jobs, but one of the things I try to explain (when somebody asks me) is that the real skill of every DS job I've had is "Taking a vaguely defined business problem and working with stakeholders to come up with a solution that happens to use code, math, and charts as the path to that solution." In reality, to succeed in as a Data Scientist, you have act like a Product Manager+Product Owner+Data Analyst+Software Engineer+Data Engineer+Data SME.
This is why so many attempts at "We can make your Business People Data Scientists" products haven't taken over. You might be able to take over some of the boring parts (e.g. AutoML, doing the parts that Data Scientists hated doing anyway), but I've never seen a piece of software that could tell the business users that they don't have the right data to measure what they are trying to measure.
I've even seen commercial AutoML solutions lead to business people realizing they need to hire Data Scientists. This is because once you use AutoML, you realize you need somebody who actually understands the data and the process to really trust the results.
If "hybrid finetuned multi modal language/code models" can replace what competent people in ML/DS can do, that technology is going to replace a lot more professions than just ML/DS. There's going to be a job apocalypse for lots of professions, including SWEs.
I think from a career standpoint, ML/DS is a bit of a mess because it's a new field and businesses are still trying to figure out the best way to get value out of it. So there are a lot of pain points for people working in the field. Compare that with Software Engineer, which is older and a bit more mature. But I still think ML/DS is a field worth getting into if you can sort through the noise of which jobs are good and which are crap.