I’m currently working at a Big Tech company in a network engineer capacity, but am not sure if this is what I want to be doing for the rest of my career.<p>Like a lot of people I’ve been pretty taken with recently advances in ML, and am thinking about quitting my job and taking 6 months or a year to go deep on learning the state of the art.<p>Has anyone done this and can share their experience? In fact, if anyone has done this in any field I’d be interested to hear how it went.
Since you asked for anecdote from someone in any field, I'll give you my thoughts as another SWE.<p>I quit my job to work on something I care about, and it has been the best decision of my life so far. There are no directions or signs to guide me, besides just trying and finding out.<p>Juggling a job, life stuff, and trying to fit in time for potentially life changing stuff like this is not a good idea imo.<p>I always crushed it at work (EE in every performance review, paid highest band of salary, etc.), and have chased excellence in everything I tried since some of my biggest failures in my early life which set me back years. Anyway...<p>Having a job is the average case. If you believe in yourself, and know that you are competent, do not not worry. Do you have savings (big tech? I'm sure you do) and a plan?<p>Go for it if you have savings and a plan. You can always find a job, there will always be jobs out there for people like us. If it works out, you will be happy. Don't take advice from risk averse people if you're capable!<p>Either way, if you feel deeply about something you should go for it and figure it out. Good luck!
I've done this 12 years ago. Network Engineer --> Sales Engineer --> Product Manager --> ML Researcher. Though I ended up getting a PhD in ML after I quit my PM job to pursue AI.<p>It was a different era, DL field was a lot simpler. Until about 2015 I could read all important papers in all subfields, as soon as they were published. On the other hand, today there are so many great learning resources. For example, back in 2013 I could not find a simple explanation of how RNNs worked. I emailed Schmidhuber and asked him "where can I learn the basics of RNNs". He pointed me to Alex Graves PhD thesis. Today you have lots of code to play with (all important ideas have multiple implementations), libraries are mature and user friendly, everything just works.<p>My goal from the start was to learn enough to do research, to advance the state of the art, and that requires a lot of effort. What's your goal?
Why don't you get to know the people working in AI/ML at your current employer, and ask them for advice?<p>Trust me, it's MUCH easier to network within your own company than when you are an unemployed outsider.
Why don’t you learn in your free time and save money, then switch jobs when you have done some hands-on learning? I do not recommend leaving a job right now, in this hiring climate.
Keep your job. Do this on the side. It took me two years to get some Microsoft certifications... back in the day.<p>Seems slow, but having a goal to leave the job... can be motivating on its own. And you'll get there. Then you can start applying for jobs. Either out of the company or laterally.<p>Best of luck!
IMO you should just transfer internally to a Deep Learning related team. That’s the path I’m taking, and while I wait for my new start date, I’m reading the important research papers etc. It shouldn’t take you as long as you think.
I did this, mostly to start and wrap my MSCS (ML focus) and augment my data science skills. Would I have made the same decision in today’s hiring environment? Perhaps not. I was also solving for some other goals. Feel free to DM.<p>I’d wager that networking into a role would be best for most in your position given the market.<p>You might also consider trying to cut your downtime to allow for a retreat like the Recurse Center to take a focused leave while maintaining employment (still comes with risk).
I didn’t take a year fully off but last year was slow in my consulting work so I did a deep dive on the emerging LLM area.<p>One recommendation - get a beefy Mac laptop so you can run LLM’s locally. I got an M2 with 96G RAM. Makes a huge difference to your thinking about LLM’s when you can run your own and integrated it with little tasks here and there for experimenting.<p>Else I find most people think only of centralized closed LLM’s when they think of what’s possible. Severely limiting.<p>/r/LocalLlama on Reddit is a great community.
Other than that check out llama.cpp and ggml.cpp and the whole ecosystem around that.<p>Cheers and good luck. Ping me on DM if you want more pointers.