The Sad Reality of AI Job Market w/ ML Engineer

I'm timing this perfectly I'm going to finish up in a year or when I graduate there'll just be 50 people begging to give me a job and I'll make a millionar to retire 10 and tenner hi you're part of s's team and you're machine learning engineer and data scientist I'm really curious first about the recent AI hype in your experience everything what you see as machine learning engineer what are common misconceptions people have about AI so how do you manage those expectations when you have to work on a project or with a client yeah so

there's I can either speak to my recent experience of someone trying to get a job in this and then maybe we turn them more about what the job entails so yeah this didn't it was very fortuitous timing for me to be a graduate around this time of iight machine learning was definitely growing and popularity when I made the transition to the master that was a deliberate career move and then when I was halfway through my degree got gbt came out and all M generators got really popular and the hype would Endor this and I thought

great I'm timing this perfectly I'm going to finish up in a year when I graduate there'll just be 50 people begging to give me a job and I'll make a million dollars and retire in 10 years and that reality Came Crashing Down pretty quickly and I think the primary reason is that people when I was looking for a job every company wanted someone to either start their AI team and they wanted someone with a new of experience public in E that sort of thing so everyone wants machine learning engineering everyone wants AI even if they

might not know how it's so in line with a Stanford AI index report the common misconception is that oh my God everyone is hiring in AI but actually the boom was really in 2022 and then 23 saw across the board me mean it's not just us but also other countries having like a dip in demand from the report they highlighted that big tech companies cut on their staff and we saw big layoffs for me the other one kind of obvious was that people probably already hired their machine learning engineers and build teams in 2022 and

their demand was not as big in 2023 what is your feeling why these job positions are down and yeah obviously a senior positions high in demand I think it's probably a combination of factors I think you are correct in the sense that companies who maybe are a bit more rash with their decisions didn't want to be left behind didn't want to be left out and just started hire like crazy and probably said we'll figure out what to do with them later but if we don't hire more competitors well we don't want to be I think

another aspect of it is that a lot of people think the ey like a silver bullet where all you have to do is just hire a couple people like me and say here's some data go build something and then you're going to increase your company Revenue by 30% and that is simply not the case I think it's a lot harder in company to realize to actually build pable value for a company especially a large company and this is one of the problems in the industry and I think one of the problems with how shean learning

is taught I was taught a lot of theory about missan learning in AI getting d then dating gr really how it works talking pretty high level linear algebra calculus probability statistics and then theory on how these large model work and that's all well and good for doing a res project for your degree it's fine for Academia but in the real world you need someone who can not only design a model and play around with the parameters until you get a good predict on your cast set i' actually integrate that into a generally pretty sophisticated and

complicated company infrastructure and that is a whole other set of skills that people don't actually learn in school or really in other applications as well because people may go in with very solid knowledge and ability in terms of actually doing model they're much moreor about how to actually take a model that works and then integrate it into company's software ecosystem and then maintain that model to the point where where it can actually produce SEL value for a company that it's worth the exorbitant price that these companies pay calories are quite high as well the productivity

of the modeling has to justify the out with us at simp Minds that it's in alignment what we saw in really 2023 to be honest that everyone wanted AI but what we found that when you come in a company they have all this existing software all the processes not really even data strategy in that sense and then we're like oh just do this magical thing yeah and what becomes that the integration becomes the challenge they like what you bring up their their integration because that's I don't think that's much talked about because everyone is focused

as just machine learning solves everything yeah and the integration is way more complicated than people I think understand and that's why there's actually a big push in the fields in terms of company they want people who are not just data side who aren't just tinkerers with but people were also Eng and I think that's why learning engineer a machine learning engineer not whing up a model with high torch and getting good results on a predictive task and saying okay job well done can we con create a model into infrastructure means you have to host this

somewhere the same way you host website you have to figure out how the data pipeline is going of work are you ingesting data continuously streaming that every now and then how do you clean the data how do you store the data how do you then pass your data to model for a lot of companies the time it takes to make a prediction critical may say if you need a prediction fast that is an engineering concern as well because a lot of times these models are quite slow so it's another engineering to figure out how are

we going to handle inst predictions at scale and still make sure the customer the user it's a prediction quickly how you monitor model it changes over time and your model was trained on data from a year ago eventually your model performance there's a good chance it drifts in a negative Direction in the performance hits worse over time so how do you make sure you're monitoring the model's predictions and then do you have a plan to retra the model and these are all kinds of things that you don't wear usually at school maybe at class you

don't wear it with other educational resource but they're arguably more important than being really good at creating a predictive model in the first place before machine learning Engineers would go into Academia like doing research where all that theory can actually applied and you can be tinkering with just math problems but now uh there is I think from 35% to 70% growth for engineers to actually go into industry because this is where money is this is where computer is and it's much more interesting but the traditional education not necessarily prepared for this shift right no I'm

sure it's better at other schools then probably better at institutions that are more geared for engineering like engineering institutions they probably teaching more of these practical field personally I went to the McGill University and then I also went to the research institute my masters called the Montreal in learning algorithm called mup and both of these institutions care primarily about acade they are trying to prepare their student to transition after this degree to the next degree in their academic career so they want to prepare you to do a p i don't necessarily want to prepare you

to your de apply it as a large company which is why they focus so heavily on the theory and things that are important for publishing scientific paper and less so in the actual practical skills and I imagine this is not just an issue institutions I would that probably across the board in Academia as I mentioned to you the machine learning roles they actually fell percentage wise in 2023 the roles that emerged and is actually growing is generative artificial intelligence Chad GPT being a skill requirement on a job post prompt engineering closely followed that and what

was impressive to me that prompt engineering there were more jobs in prompt engineering than in generative ADV virtual networks as a requirement of a scale yeah that's not necessarily surprising to me again are a specific sub class of deep learning model with specific applications I'm wondering maybe a company had use again internally and then HR knew that and just said okay to be an expert job but it's pretty specific whereas engineering even a cat I love it okay I'm keeping this lastly TR a thought oh drom toering is universally useful for pretty much any job

because these models are so phenomenal just with a little bit of n how to use them you can increase your productivity in so many F of your work with pretty minimal effort so it's definitely more widely applicable in Universal skill than something as niche as game what was your kind of in into prompt engineering was it part of your education was it existing term around it was definitely not a form part of her education my education for my master focused on the and learning Ser wiring developing models work that sort of thing personally I first

became aware of it uh I'd say halfway to my degree I remember one time I was really struggling with understanding this block of code and I believe it was a block of code for calculating a prior for a diffusion model had code instruction keep learning so I was really struggling with it and my wife just said can you just put code in the chat GPT and have it explain it and I said I think that works with simple things but this is you know this is pretty high level it's essentially mathematical programing for a pretty

complicated mathematical process I very skeptical but I said what the heck let me try it I put it in I said hey blame it I was blown away how perfectly and intuitive was able to take something so technical something so daunting and unapproachable someone like me who is has a solid math background but it's not super Advanced compared to people really working in this field and now with the light bul for me where I realized maybe I need to get rid of my se otions but Kon can't do and if I believe that it even

has a small chance that it can come T I'm just going to try and more often than not it doesn't always work but more often than not it's going to immediately indicate to me that there is some value here and then you get to the process of okay is it not working because it can't do this or is it not working because you're not giving it the proper instructions to get it to do that was personally my progression so once I got to that point I said okay now I need get better at telling chat

GPT which is what I would using how to help me and then you start reading stuff personally I've learned a lot from people here then because there's some incredible prompters here and they really just when I thought I was getting good at prompt engineer look at some of the work that they are doing I say okay actually have no these people really their stuff they're going way deeper than I possible and then yeah I'd say once you get to that point you've done the easy stuff but then you really have to learn the techniques for

how to extract that extra and once you dedicate yourself to learning even B problem engineering can take you way further then then why day for me personally prompting ruined my experience with chity it completely removed with magic and I vividly remember that I was asking a question and I I just thought okay so just statistic so I'm asked this question once and I got this answer what if I just ask the same question do I get the same answer and I was just curious how same answer can change then I learned about temperature and like

top p and stuff like that but it done to me like okay so if I'm playing with different settings but what if I ask the same question thousand times okay and then I saw hallucination that magically appears so I plugged um plugged API to Google Sheets and I was just like same question dragged itself down and I was like oh so it's just almost luck what answer I get or can I control what answer I get right and this was like you know I started playing with different variables I remember we were debating very long

is it better to say act as a copywriter or act as blog writer what gives better results anyway so I'm going tangent here but sounds it's it's always for me fascinating to hear people's experience with prompt engineering yeah but what you were doing sounds like that could be like a master thesis it's really interesting way to try to break it down and study it and analyze it