| That's a lot of questions so I'll try to answer one by one. > Whats your recommendation for backend engineers that want to transition to ml engineers? I get this a lot, and my first question would be, why do you want to work in ML? Is it because it's "cool"? because it's in high demand? because it pays a lot? for the second two points, see my comment. For the first point, I'm pretty cynical about it. At the end of the day it's a dev job not very different from other domains. Depending on where you end up, your day to day might look the same, but with focus in ML systems. Nevertheless, if you really want to transition, I still think the easiest way is to do an internal transfer to a team that does ML. That's how I got this job. If you are a good engineer, most ML teams will be happy to have you and teach you the skills on the job. Of course maybe there is no ML team in your current company. If you are in a small company you might try to influence leadership to start ML initiatives and get some experience. I've seen many candidates do this: they start an ML project in their current company, and after 6-12 months they look for a job elsewhere. This is a variant of CV driven development, and sadly often these initiatives are hamfisted and don't really solve business problems. Otherwise you might try to get a job in ML in another company. Lucky for you I found some companies that hire engineers in ML or ML adjacent jobs using a standard software dev loop, i.e. no special ML round, although these difficult to find. The other option is to try to learn enough ML to pass an interview. I won't lie, this is doable, but the deck is stacked against you. The job market is pretty insane right now, and you'll be competing with people with masters and PhD's in ML, plus engineers with years of experience in the field. I believe it's fair to say that pure theoretical knowledge won't get a job, much less unless you have prestigious credentials. Instead I would focus on a blend of applied theory and personal projects. Probably it would be best to choose one ML framework and double down on it to increase your chance of finding a job that requires that particular framework. For theory there are tons of courses and resources online, although I tend to like more walkthroughs and tutorials that guide you on building a system rather than pure theory. For practical experience, the sky is the limit. As I said there are tutorials online that guide you on a project, but that will almost certainly not be enough. You can try your hand at Kaggle competitions, and at some point you can just choose some personal project that interests you and try to build on it. I hope any of this helps and best of luck to you. Feel free to DM me if you want to know more. |