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Funny there's no mention of ML without a PHD. Anyone done that?
I'm a Data Scientist who builds ML models. My bachelor's is in psychology, I just studied and learned how these algorithms work.
I moved to a Machine Learning Engineer role a few years back. I just did an internal transfer to a ML team with no previous knowledge. The team trusted me to pick up whatever I needed on the job. Now I interview tons of people for MLE roles. Trying to get in "through the front door" is incredibly competitive. Not only you have to prove you are a great engineer, but also good ML knowledge. You'd be competing with peop…
Are these academic types great engineers? Do you find that the academics have a major edge in domain knowledge over self taught programmer?
Personally I don't mind the candidate's background so far as they can do the job (how the interview is a sucky way to determine this is another topic of conversation).
Has anyone tried asking GPT-3 or one of its cousins?
https://twitter.com/bsunter/status/1535730704138444801?s=21&...
Funny there's no mention of ML without a PHD. Anyone done that?
Earlier quoted context omitted.
I want to do backend development next, I find it much more interesting (and more rewarding to work on based on the things I've done).
This is literally opposite of me. Would you mind elaborating a bit on the negatives of working on the ML side of things? Definitely not saying your wrong or that it's better than backend dev (it's probably just personal preference). But as someone considering it, I'd like to hear the good and bad of each type of role.
- Scientists dont always make the best 'clients'. The requirements you spend months implementing may be completely obsolete by the time you are done and then completely unused. - You often dont understand or are made aware of the impact of your work. - Its challenging to compete with Masters/Phd graduates who have spent years delving into ML. Entry-level knowledge only takes you so far. So its more likely that you wont work on cutting edge ML research. - MLE work in my experience has been mostly around infrastructure management and data security. Again it has interesting challenges and hard problems to solve but with the speed of the AI world, it all boils down to facilitating the scientists and researchers as much as you can
I moved to a Machine Learning Engineer role a few years back. I just did an internal transfer to a ML team with no previous knowledge. The team trusted me to pick up whatever I needed on the job. Now I interview tons of people for MLE roles. Trying to get in "through the front door" is incredibly competitive. Not only you have to prove you are a great engineer, but also good ML knowledge. You'd be competing with peop…
As a Machine Learning Engineer what tools do you use on a regular basis?
I moved to a Machine Learning Engineer role a few years back. I just did an internal transfer to a ML team with no previous knowledge. The team trusted me to pick up whatever I needed on the job. Now I interview tons of people for MLE roles. Trying to get in "through the front door" is incredibly competitive. Not only you have to prove you are a great engineer, but also good ML knowledge. You'd be competing with peop…
Personally the path of becoming an excellent engineer is the best path (possibly hardest) to take on any situation, precisely because of the versatility in scenarios like this. I'm by no means an excellent engineer, but I like to plan and approach opportunities thinking someday I'll be one.
Fake it until you make it. If anyone catches you, tell them you're building a career model.
ML jobs are split into theoretical and practical.
Theoretical involves building proprietary models based on academic papers, and training them. This is where the PhDs are going.
Practical involves deploying ML models, either in the cloud or on devices. This doesn't require the heavy theory that is still rather new in university, it is more about application programming.
The theoretical jobs pay a lot more, the practical jobs just require a cursory knowledge of ML, and not the nuts and bolts. The latter requires a lot more patience to understand the explosion of inference hardware (esp. Nvidia's convoluted tooling).
Theory: can't unless you have a phd.
Practical: learn python and tensorflow, C++, and devops.
Earlier quoted context omitted.
You don't need a PhD for DS/ML Engineer. Even at FAANG, even in their research labs. Usually a PhD is only in the requirements for Research Scientist (RS). That said, I did a PhD (and am now a RS). It's a fantastic opportunity to learn fully focused during a few years. But if your sole objective is the career (which is ok!), don't do a PhD. There are much easier ways to break into ML industry.
To add to this, companies at Google-scale tend to have a huge variety of ML related jobs, ranging from low level things like optimising libraries for different hardware, to the more general research positions where people are working on their own pet projects. Plus everything in between - data management and curation for training models that get used in production, people who try and figure out how to productionise c…
There is nothing quite like having a world-class researcher ask you to figure out why their model is exploding, and tracking down the crazy things that happen on TPUs when their math isn't absolutely perfect, then helping them fix it, and see them publish their results (or put them in prod). Or knowing enough software and hardware to debug a tensorflow TPU problem with an oscilloscope connected to the voltage regulator in a hardware lab.
Personally, i gained these skills over a long period starting in the mid-90s (working on machiine learning, and then later HPC for biology, and ultimately back to machine learning). But I am a slow learner. probably the shortest path is to get accepted to a major university and do really well in your ML and CS classes, then parlay that into a job in a FAAMG, then figure out what you want to do with all your skillz.