Kudos for working so hard on side projects to prepare your ML career!

I think that's the way to go. If possible, try to get some feedback from experienced ML engineers on code quality, methodology and used libraries. Make sure to at least learn the basics of all the libraries that your potential employer could require.

If you can demonstrate your skills (perhaps in a live coding test), you can make it! I have colleagues who worked in the humanities before learning programming and becoming ML engineers, so it's possible :)

Side projects are a great step. If possible, find some ways to apply those skills in your current role as well. For example, trying to predict the number of incidents in the next week based on the changelog from the previous week, or trying to predict whether a release will affect latency. These are just a couple examples to get you thinking - you'd know better than I what would make sense in your role.

The combination of strong side projects and on-job experimentation with machine learning should be enough to get you through an initial recruiter screen for an entry-level ML role, so long as you're using technologies that the role is looking for. After that it's really up to the technical and behavioral assessments.

And just to set expectations, it's doable but not easy. I'd guess it'd take around 20h/week of practice and learning for 6-12 months, then about 20h/week of practice/learning for interviews for 3-6 months. It'll be easier for some people and harder for others; I just don't want to give you false hope that it's typical to switch roles in just a couple of months.

Good luck!

If you're gunning for ML research, that will be tough.

ML engineer, though? Don't overthink it--just find opportunities that you find interesting and apply. Yes, every company is of course going to prefer unicorn candidates who have "been there, done that", but the need for ML engineers has exploded (demand > supply), so they can't be so choosey.

And, in reality, for most companies hiring ML engineers, what they really need, first and foremost, is people who will be excited doing SWE work on ML pipelines...which is really another way to say SWE work (which happens to involve ML). So if you're a strong software engineer, a lot of places will immediately be interested in considering you for such a role.

(To be clear, I don't say the above to "knock" ML engineering, companies doing such work, etc.--rather just commenting that the reality is that at many companies shipping "real" ML products to production, the day-to-day pains are often about data pipelines breaking, scaling problems, software version incompatibilities, etc.

I.e., it isn't about exclusively solving deep Pytorch voodoo or similar.

Rather, much of it is "classic" SWE concerns that touch ML systems.

This is a good thing for you, as they know that they can hire someone smart and you can be productive pretty quickly--and then you can learn the more obnoxious ins-and-outs of paper implementation / why CUDA hates you / etc. as you grow.

Good luck!)

Any resources you would suggest I look into that would make me shine? More resources are always more helpful than what I already got.

Thank you. Any resources you would suggest I look into that would make me shine? More resources are always more helpful than what I already got.

Thank you. Any resources you would suggest I look into that would make me shine? More resources are always more helpful than what I already got.

There's Andrew Ng's Coursera class and the related classes if you haven't seen that yet. I think there's a full specialization now. He's also got a decent starter PDF called Machine Learning Yearning.

I've heard that the Fast.ai lectures are good, though I haven't watched them myself.

Google has some great online reading. I like the People + AI guidebook cause it focuses on how to apply machine learning, and that's an area that's often overlooked.

Kaggle and other online competitions are a great place to learn and grow. I'd suggest starting with some of the easy ones that have tutorials, and then looking for competitions that you're passionate about. For instance, years ago I ran into a competition run by the European Space Agency -- that motivated me to push harder and learn more.

If you can find projects to team up with others, that will help you a lot as well. DataKind is an example of that, but I don't think they have much ML work. I'm not sure if hackathons still exist but those can be another great way to learn quickly.

To get inspiration about projects that may be relevant for your current role, I'd suggest doing some searches on Google Scholar and reading those papers, then finding the papers they cite that are interesting. And then finding the most popular papers that cite them. There's almost certainly some interesting work in your area and the trick is figuring out what things are called so you can search.

Thank you