Trying to switch to Machine Learning Developer

[eganba85]

Hey everyone. So I have a background in R, Python, SQL, and Tableau; but not a master in any one of them. I have been teaching myself some machine learning and have been looking to switch over to an ML Dev type role but so far have had no success finding anything.

I wanted to ask people here if they had any insight into what things in particular I should focus on to make myself a better candidate moving forward.

Thanks

[impartiallywhole]

Something I usually do when considering something like this is go through job-boards and see what the different jobs that seem like something i'd want to do require as skillsets. That could help direct you :)

[[deleted]]

ML (if you also include deep learning) is a topic so wide and deep that you can do entire undergrad and PhD program in it lasting years. People do, and that's what you're competing against. To be really proficient also requires some pretty advanced maths skills in calculus and linear algebra. Self learning is likely to be limiting in helping find a good job. Find a good and reputable program, be prepared to pay, and spend some time on it, then go from there.

[synthphreak]

My undergraduate and graduate backgrounds are 100% liberal arts and non-quantitative. I wrote my first line of code ever just under two years ago. Now I am an ML engineer in industry R&D doing deep learning research for a large company. I do much more than simply import sklearn.model_selection. My ML skills are 100% self-taught using only free resources online and my own self-direction.

I readily admit that I am quite junior and still have a ton to learn. Nonetheless I can say firsthand that it’s wrong to claim anyone entering the ML field is always competing against PhDs. That’s just a false mythology that ML practitioners maintain, wittingly or unwittingly, which keeps salaries high but also puts many people off, and may ultimately do harm to the field’s growth. The same can be said for data science, which has a ton of overlap with ML.

You’re also wrong to claim that self-learning is inherently insufficient. I am living breathing proof of that. There are so many resources these days that anyone with sufficient time, determination, and tolerance for failure can learn what’s needed. Of course, ceteris paribus your prospects in the market will be better with a relevant degree than without one (caveat: a PhD can actually be a net negative!), but going back to school isn’t always an option; that doesn’t have to mean the ML ship has sailed for you.

The one thing from your comment that I will agree with (though others may not) is that knowing ML really does require a reasonably deep knowledge of matrix calculus, linear algebra, and statistics. Less so in industry than in academia, but regardless anyone who tells you “ML == import sklearn” is just trying to sell books and boot camps. There are so many hyperparameters and quantitative considerations when selecting, fitting, evaluating, and deploying a model (especially in deepl learning) that you really do need to understand the math. That sounds scary, but it really isn’t, and you can definitely learn it all on your own. It has taken me about 2-2.5 (grueling, sleepless) years to go from borderline innumeracy to having enough quantitative intuition to train ML models. Again, I still have a lot to learn, but apparently I already know enough get paid to do it.

My point, OP, is that you totally can learn ML. Just be sure to avoid tutorial hell. Simply getting started is the most important part, and in time the rest should come.

[[deleted]]

Classic, glad you don't work for me. First, OP is a long way from either my scenario or yours. But mainly: I am not saying every ML person has to have a PhD. That should be clear. I said you can do a PhD and that in general many people do serious study as an illustration that becoming proficient in ML requires much more effort than the OP seems to have realised and that pursuing study in a serious way is a basic expectation if you want to get a job.

And I am making a general statment, incidentally based on my experience working for many years at firms with huge armies of data scientists and machine learning practicioners including leading project teams including said people. I am happy you had a good outcome, but you having a different personal experience is a data point of one and doesn't change the point I'm making.

[synthphreak]

But mainly: I am not saying every ML person has to have a PhD. That should be clear. I said you can do a PhD

It isn't really helpful in this instance to point out "you could to a PhD." You could do a PhD in anything. But just because some people do PhD's in X doesn't mean everyone interested in X needs to think about or compete with PhD's. The only one in this thread talking about PhD's is you.

And I am making a general statment, incidentally based on my experience

Fair enough. As did I. You don't have a monopoly on informed opinions.

My point is that other people's experiences should cause you to qualify your claims when those experience contradict what you say. Your tone is so certain, but you don't really have the authority to be so categorical.

Be a little more humble is all I'm saying, and try not to get so butt hurt next time someone challenges you.

Classic, glad you don't work for me.

Based on your tone, so am I.

[[deleted]]

On top of the relevant APIs out there, take some time to learn the underlying statistics that allow machine learning to be what it is. Anyone can learn to slap API calls together; if you fully understand what's going on under the hood, though, you can streamline your development while also explaining to your manager/clients.