Are you a person doing the fast.ai course or learning machine learning in general with the intent of making a living out of it without a domain expertise? By that I mean you are not a radiologist who wants to learn about segmentation, not a musician who wants to leverage RNNs, not an artist who wants to explore GANs. You are a developer or worse yet someone who is learning to code who foresees getting a job in the wonderful field of data science without a related degree? Extra points if you live in the middle of nowhere for the startup scene, which AFAICT for people living in Europe is everywhere but Berlin or London.
Are there more people like this? If so, what is your story? Where are you right now? What are your thoughts and hopes? I am genuinely curious. I went through a recent āintroduce yourselfā thread on these forums and there were either forward thinking students of CS / ML working towards acquiring practical skills or software / ML professionals expanding their skill set. Or people entering the field with primary expertise in something else.
I know this is not what fast.ai courses claim to enable, but I have a worrisome suspicion there might be quite a few people in a boat like this, people willing to make a living through their ML competency acquired online. I recall how DataCamp would advertise itself, as offering the entry way into ML. They quoted the statistics how much ML people made but they never mentioned how many of their customers were able to make the transition thanks to their courses. I am quite convinced that number must have been very close to 0 (they thankfully now seem to have pivoted away from positioning themselves like this).
What I am worried about is there might exist a large, silent group of people doing MOOCs, doing the fast.ai course, but never managing to break into the field. And we never hear from them.
Essentially, above I described a person exactly like myself but one who received less support or got less lucky than me. Or was less persistent. At this point, despite having no degree and learning to code in my thirties, I am fairly sure I would be able to support my family from ML and could do so through remote work or freelancing.
To elaborate a bit more on the aspect of chances, I once heard that to understand the American society, itās best to view it as consisting of a very small minority of very rich people and everyone else who thinks they are millionaires in the making and adopts the mindset of people born to wealth.
To a European this is a heart breaking perspective as it is apparent how much suffering such prevalent belief in the American dream brings about (namely, that so little attention is given to the well being of people doing regular, down to earth jobs, which is the vast majority of a society). Everyone loves the ārags to richesā story and if you take a population that is large enough you are bound to get some really weird outcomes at the tails of the distribution. Toss a fair coin 10 times in a row and run 5000 of such experiments. You are more than 99% certain you will get 10 heads in a row at least once!
But the much more interesting question is what happens to the overwhelming majority of coin tosses, that is when you get 5 heads and 5 tails or something there about.
So my question to you is - are you a person who wants to get your first developer / IT job in data science? Would you like to do the work remotely? If so, maybe it would make sense to talk more about the odds of your flips. Or what you can do to toss the coin more in you favor, I think I now am uniquely equipped to speak to that. Or maybe there are companies looking for people like us and we donāt even know they exist?
Sir i am living in delhi India. Has a science and business degree.And working in a traditional logistics company. Very keen to break into ML taken fast ai lectures. Working on @kaggle,@ZindiAfrica, @drivendataorg competitions
Also in mid thirties now so really hard to transition i guess but still trying post doing a full time job
I studied biotechnology and I didnāt even finished my degree. I started my journey into ML because I really enjoyed coding and math. Thanks to MOOCs like fastai and others I manage to earn some Udacity scholarships and now I am on the point where Iām getting some job interviews.
I think is possible to get a job without any degree but it requires some extra work to make for the signal a degree certification has, which is understandable because recruiters donāt have the time to go deep into every person background, but make statements like Google saying they donāt require PhDs or even a degree a bit false.
Hi! I have a major in computer science but never learnt anything about ML at university. I achieved my knowledge on ML by the help of many many moocs.
I think there is a very big business on āhow to get richā. Just have a look at all the blogs, eBooks, courses, etc. on this topic. But the only ones that probably get richt are those providing these materials. Take Udacity for example. There are many courses that sound quite nice but all the courses I have made provide just a very basic low level knowledge yet they are advertising how much Engineers makeā¦ I do not really think you get a job paid 200k after some weeks of online courses.
I really like the fast.ai moocs because they just claim to get you started with the fast.ai framework and do not provide endless wealth. Cheers to Jeremy!
Thank you for asking this question. I wouldnāt have shared my opinions but I think some might find it relevant.
I havenāt had positive education related experiences so far. So much that Iāve been unequipped for a lot of jobs that I wish to pursue. It also became a proponent for my depression which further stripped away my motivation and desire to continue making some effort to up-skill properly. Been years in such a state.
Iāve been stuck in the loop of taking online courses before but not really going anywhere. Itās not been easy for me for personal reasons that I state above. Worse, Iāll be moving to a new country without a job in hand and itās extremely daunting to know Iām so far from being ready. I donāt even know if this is for me or not at this point.
I tried fast.ai couple of times before but never managed to reach far enough. I was always stuck between - āthis doesnāt seem in depth to help me with a careerā to āthis doesnāt offer the kind of support to help me overcome my own personal and technical obstaclesā.
Itās worse to see so many others who do manage to genuinely put in so much more effort still struggle to get jobs.
Hello, just wanted to share my 2 cents and experience so far trying to transition into a āData Scienceā role. Since being a ārelativelyā new field, I believe many companies do not quite understand what they want. They just say they want data scientist and require a phd to be safe. In my humble opinion, I believe there is a big difference between a data scientist and what is more commonly needed, an ML Engineer. The former is the one that does the research, has the deep math background, and comes up with new and innovative models and processes. An ML engineer to me is an implementer. Someone that has a diverse background in coding, data pipelines and how models work. They can take a paper and put it to production using various tools. They are not necessarily reinventing the wheel. This position is much less strict when it comes to degrees (yours truly does not have one).
I will end my rant with a recent story that illustrates this. I was at a company and tasked with detecting anomalies while drilling a well. We tried various time series methods (1d con, RNN) but ended up taking a value trace, graphing it and running image segmentation on it. We used a unet built for bio medical image segmentation and repurposed it. We didnāt invent the unet methodology, but we knew how to use it for our specific problem. This is what I believe a majority of companies really want/need. They need someone to take their data and put it to use. Of course, some really do need researchers but in the past couple years I have noticed these companies to be in the minority.
Anyway, sorry for the wall of text but I can share in the frustration of the ādegree neededā misconceptions.
Thank you very much for all your wonderful posts Between them and the replies on Twitter I have certainly learned a lot, a lot of food for thought. It is also interesting how similar our experience is, I completely hear you on the points that you raise.
I guess I only started to do ML approximately full time last year when I was 36 Still not sure what the chances of making that happen are, but fingers crossed for your journey!
Ha!
You seem to be more understanding of recruiters than I am 100% agree on the degree still having a lot of weight. Same with past employment in a role, which is hard to be able to demonstrate when you are trying to do something new professionally, something you havenāt done before.
One other related observation that I have, is how LinkedIn, which has become the defacto standard for many job opportunities, makes it extremely hard to put together a coherent resume if you want to put your foot in the door in a new field. It puts past employment and education front and center and the piece for projects you worked on feels bolted on and there is no way to emphasize it. At least that was the state of affairs a couple of months ago when I tried it, maybe this changed but I doubt it.
Ah yes! The good olā ālet me tell you how YOU can get rich while I make a lot of money in the process doing something completely elseā. A fun example of this are books on frugality and investing into the stock market as a vehicle for wealth creation where the author themselves makes money on selling said books and not using the formula they advocate
Depending how you define wealth! If wealth is having a supportive group of people and being in an environment promoting personal growth while having fun, than I feel we all on these forums are very wealthy! But just joking, I completely see your point.
I can certainly relate to this sentiment. I just wonder if entering other development professions requires comparable amount of effort. I can see how that can be for instance for the game industry, where probably one needs to invest even more time into becoming employable and than their work life still has a very large likelihood of sucking really badlyā¦
The crux of the matter for me, where I guess I was going with my initial questions, is should someone approach me and tell me āRadek, I want to do machine learning, but I am only starting to learn to code, and I want to make money doing that at some pointā I am not sure what I would tell them. I could tell them how to do it, probably provide some really good advice that would seem counterintuitive to them, but I am not sure what I would say to whether they should embark on this journey in the first place if their intention is to earn a living from their coding skills / new technical role.
On one hand, many people seem to pull it off to great effect. Despite what one can say about hiring practices of companies in general, there are still many safe havens for people with non traditional backgrounds. But would one be better of going initially for some other role, say that of an automated tester or something related to web dev since that job market is so, so large and seems way more mature when it comes to junior roles and remote work? I do not know.
On one hand I feel that this is something that deserves to be talked about, to help people navigate the landscape who might be trying to enter the field starting from no coding skills, who intend to make ML their primary expertise. On the other hand, maybe all is well. Many people still succeed in pulling off their moonshot and never have I heard the fast.ai courses advertised as enabling such a transition. Which is really cool, because I feel that the false ideas that BaseCamp used to convey through their advertising was definitely harmful.
I still donāt know what to think but all the stories shared definitely provide a lot of food for thought.
Yes And they probably do so at their and potential employeeās peril!
Thank you all so much for sharing your amazing stories with me I definitely know way more now than I did two days ago, also a lot to think about for meā¦
Iām currently a third year Mathematics student (Pure Math, mostly unrelated to ML) at a top tier college in India (BITS Pilani. Although itās isolated and quite literally in the middle of nowhere).
The college doesnāt really teach practical coding skills, and neither are there any great ML courses offered here.
Iāve been mostly learning all of this through online courses like FastAI, CS231n etc. and in doing this Iāve realized certain things:
Most online courses offered on Coursera, Udacity, etc. are almost always very introductory regardless of what they claim. This is because they have to make sure that every average joe on the internet is able to understand the content. This makes sense too.
You can barely get any deep understanding of any hard ML concept through online courses. Taking university courses that are offered online (like CS231n, FastAI, etc.) is AFAIK the only way to get a deep understanding of the field.
Patience is underrated.
Iām currently working an AI Researcher in a Space-tech startup (Pixxel) that is building a constellation of earth-imaging small satellites to provide an entirely new kind of dataset of the earth that todayās satellites arenāt capable of.
This has been made possible entirely because a senior of mine in college was brave enough to take on this challenge and made it into a start-up (incubated at Caltech/NASAās Jet Propulsion Lab) and he happened to be a friend of mine who let me in on the team. At the time of joining, I had only completed Andrew Ngās introductory (but great) ML course.
But, the AI work here isnāt currently that great (right now). Weāll only properly start doing anything once our first satellite is up there and we get a stream of data coming in.
Right now Iād be willing to do remote work in any organization that can offer me a good learning experience. But thatās just while Iām in college. After college, Iād prefer working at a physical location.
That seems to be a very common theme behind any even the slightest trace of success Persistence is key.
The value of personal connections! I am probably a little bit of a hermit myself so it might be a bit weird for me to say this, but being part of a community is so super important!
This is something I completely didnāt understand when starting the fast.ai course.The flow of ideas, learning from others and being able to participate on projects where we would never have the chance if the personal connection wasnāt there, if all we relied on were role ads and the strength of our CV, so super important
Great to hear from you Akash, thx a lot for sharing a bit more about your background!
I have a few interesting personal stories and outside of it, this might cause some serious ātraditionalā academics to be very mad at me, but Iām sure they donāt hangout at the Fastai forums.
I did a CS Degree-a bachelors. Wow, right-I had a great start?
No! Not at all-it still surprisingly intimidates many people. Iāll be honest, I studied CS from one of the Top ātierā schools in India. It didnāt make me a better coder, it didnāt make me a better theory person either-so does a CS Background help? I really donāt know!
I hated every single lecture-Iād go to ends of earth to avoid and then somehow hack my way around to getting good grades. All the while, Iād hangout on the Fastai forums and hack my way around the material and keep failing at that too.
Did I do all of this to get a job in the ML World?
No! I was genuinely bored at the syllabus from the ages of dinosaurs at my Uni and I wanted to feel like Iron Man which Jeremy really does make us feel like at sometimes.
At one point, I had an urge to find ābigger projectsā. I would search slack groups, keep pinging people and joining hands-thats how I started Kaggle too.
At one point, someone suggested a project and a price! Thatās when I realised: OH, WAIT!? Youāll pay me to do this?
I keep reflecting upon this and hiring-I think hiring anywhere is broken and thatās why you need a referral or a personal suggestion. The CEO of Lambda school in an interview mentioned: If anywhere you need to press apply for a job, just donāt. The value of personal connections is what is most important here.
So Back to the original question of Do you need a degree?
Quite often during my Uni days-Iād apply to roles without uploading my resume and mention of the ideas that Iād be aware of, Iād get to final rounds quite a few number of times. I donāt think this means Iām smart, it shows hiring can be very very broken.
But, How do you make yourself stand out against someone who has patiently done a PhD and has a huge degree to prove it?
Not everyone can afford to go to such extents in life, what you need is to give them a strong signal.
Win a Kaggle Comp, Create a project similar to what their company is building. Go to the extent of adding a feature to their product and then reaching out, these are stories that I get to see on Twitter and I believe will work really well.
But Why am I talking about jobs?
People like @radek and myself spend a lot of time on the Fastai forums or doing ML. Which means that you really enjoy doing this. The luckiest man on the planet is who can get paid doing what they love.
So maybe you want a job or maybe not. Or Maybe you spend so much time on the course, fall in love, discover you want to do this for the rest of your life and figure out some way to continue doing it.
@Even, shared his journey with me on an interview: He got super interested in Deep Learning in Tabular Data. Ask any Kaggle GM as well, Deep Learning applied to Tabular would not be their first answer.
But Even found a very interesting Kaggle Problem and got obsessed with trying DL on it. No doubt it must have taken a lot of brains and efforts but the idea seems questionable, right?
And Magically at the same time, Rapids team came up-where he is doing amazing work now.
Iām sure Even didnāt start the work hoping to do it at his job.
Iām convinced a large majority of people that I had the chance to have chai and ask stupid questions with didnāt do it for getting a job. It was heavily passion driven.
Take Kaggle for ex: You spend 100-200 man hours working on a problem during the night, without getting paid.
Why?
Secondly, How!?
Training ML Models and given the fieldās pace is very frustrating-so you need to be obsessed.
Chances are, youāre working on an idea that youāre obsessed with and hasnāt reached the masses yet, which means itāll involve a large number of times of questioning your existence, going back to what you were doing earlier or maybe just abandoning the field.
Unfortunately ML Models fail silently and they donāt whisper their errors so the only way to move forward as already mentioned is patience along with I must say, a lot of passion and hard work.
Not sure if I answered Radekās question or annoyed any Academics but the question really sparked these ideas in my head.
Interesting question, Iāve wondered how many other people are in the same boat!
Iām a relatively experienced software developer, and Iāve done this course + a bunch of others in hopes that I can do this stuff full-time at some point, because I enjoy it so much. Iāve done a bunch of courses/books, done ok on some Kaggle competitions, and even put a few ML models into production at work (recommendation algorithm, text and image classifications, etc), but never really get the chance to spend more than 5 or 10% of my time on it, unfortunately.
I havenāt had a lot of luck finding a way to do this full-time, and Iāve gotten a little discouraged by it - finding spare time to sink into this is hard when you have a bunch of young children at home, and I think I got a little burned out, so Iāve taken some time off from trying.
I think there are a few reasons I havenāt had much luck -
Location (Utah, USA). Quite a few software jobs, but not a lot in the way of machine learning. For family reasons, it isnāt really feasible for me to relocate. Iāve usually preferred to work in an office with people, but have been thinking lately that remote work might be my best option if I really want to have a chance to do more with ML.
My background isnāt anything amazing. I studied Finance, not CS or Math. While I have some experience doing this as part of my job, it is a pretty small part. Because of the limited amount of jobs and the fact that so many people are excited about this field, I know there are people who have more formal education and more experience Iām competing against. For any given backend software development or big data-type job, I generally have a 90%+ chance of getting an offer if I apply for a job. For ML jobs, more often than not, I get rejected before even getting to the phone screen.
Iām too expensive. I expect zero sympathy for this one, and there are much worse problems to have for sure. But after 15 years of software development, Iāve gotten to where I can command a pretty high salary. Given that I donāt have any formal ML training or a ton of experience, companies are understandably hesitant to pay me like I do. A younger me with no responsibilities wouldnāt have hesitated to take a pay cut to do something more interesting, but as the sole provider for a family of 5, it is a tougher sell.
I am from Belarus, neighbour to you, @radek. Have been working in analytics and media buying for 9 years already. Moved to Australia 3 years ago (btw, love this country).
First 2 years staying in AU, had been doing masters degree (not CS) + 2 jobs + moocs.
Now I have a job in media buying (because I am really good at it and I am married and donāt have any other financial support, basically can relate to 3 point by @suresk) but my heart belongs to ML. Managed to persuade CEO to buy me a DL rig so that I could spend 10% of my time on ML. There are no other data science people in the company so I am treading my own path.
Have been doing moocs and tinkering during work hours, before after work. I suck at that. My goal to suck less with every day. Will I be able to switch career to ML? Idk, but at least I am spending my life chasing a dream and I enjoy that. I also donāt want to do media buying in 5-10-15 years, thatās for sure.
Ive been self learning code and now deep learning for the past 2.5 yrs. My background originally was in the trades as a Master Electrician and eventually a business owner with multiple business management certificates. I few years back I had some health issues which lead to an idea for an health app which I started to pursue with the help of Startup Edmonton ( a community based incubator )ā¦ The app idea ultimately was shelved and I pivoted towards using Computer Vision in the Electrical/Mechanical industry. I received a grant to have the prototype built with OpenCv. I found the programmer I hired wasnāt familiar with ML and he/they ended up under delivering on the prototype but did prove doable. So I after some reaching out with no real luck on finding a partner with ML expertise I decided to continue the deep learning on my own (I did find one guy but he has yet to deliver our beta) ā¦ Fastaiās approach for me has been a god sentā¦ I have no shortage of ideas and can see so much potential with Deep Learningā¦ Having said that Im very much an Entrepreneur and my experience is that not everyone can see potential outside of their current discipline. Ive come across a lot of coders who are very skilled but lack ideas and need others to collaborate with. For me Im a very curious person and that feeds the creative side of meā¦ If the Startup fails to launch or survive I can see continuing to grow in this field for myself, Iām really enjoying the process and the play aspectā¦
You are 100% correct. Iāve seen that countless times too. Recruiters donāt understand the difference between these two roles and if I was in their place, I would be confused too as you really have to understand the technicalities to be able to tell the difference. If a recruiter doesnāt know tech at all, he will just throw his ball at a PHD researcher as itās the safer option, but far from being the adequate one since itās the type of profile which doesnāt know how to ship products or to deal āwith the mess of realityā.
You donāt built a company on research since you cannot know in advance what the outcome will be (thatās the nature of science). You build it by reusing proven techniques which have strong track record. And in order to do that, you need engineers, not researchers.
I donāt think itās the degree itself which has weight. Itās your last public experience or past employment like you said @radek (which the degree help in a way). For instance if you get a degree in CS then work in a company for 2 years, what will matter to your new recruiter at this point is your work experience instead of your degree. If you transition to say, a carpenter, your past work experience in CS wonāt matter at all. The degree is just a matter of publicly showing āIāve step foot in that area already, I know my shitā. But there are other ways to achieve that (read more below).
I know a person well enough to testify that (hey @jamesrequa ) since he has no prior background in CS and ML, heās in his thirties and was able to land on successful freelancing missions in DL.
After 2 years of freelancing with James I find itās a good way to show your experience, on platforms like Upwork where you can make yourself a reputation and once you have it, degree or not it wonāt matter, only your Upwork profile will matter. The good thing is: No one one Upwork will ask for your degree when you start, at least I didnāt see anyone asking for it so far. So to me freelancing platforms are a great way to āshow offā your skills and make your first steps.
Iām sure if James and I took the route of going straight to a full time job we would have struggled much more since we had very little to show (Moocs and co doesnāt resonate well to recruiters, only Masters and PhD or public work experience). If today Iām face to face with a recruiter Iāll tell him about my journey with the clients I had when I was freelancing, the challenge I faced and the solution I brought to the table, I donāt think Iāll tell them about my degree even once, that wouldnāt even matter to them.
Amazing replies, thank you so much for sharing your stories, learning so much from you
The frustration with the status quo, love for learning, appreciation of ML, persistence over very long periods of time, super awesome observations on what actions to take to turn the odds more in oneās favor, all is there.
Thank you @radek for the very interesting question raised. I was just like you, searching actively for a job in ML/DL.
You donāt have to follow the default linkedin way of putting the current position as headline. In fact, Udacity once emphasized on writing the headline by yourself would be much better. The instructor was a former employee in LinkedIn and worked as HR. Look at my linkedin, the headline is completely customized.
For the ML job, I was just like youā¦ My dream was getting a job in datascience/ML. However, after reading the experience of several others who worked as datascientist or ML engineers, I realized that at most companies we are asking for troubles for such kind of job. Of course most blog posts will state how ML jobs are awesome, and everywhere it is praised, but keep in mind, there is a strong bias to post only positive experiences (either revealing an honest true experience or perhaps even making it up), since the incentive is always to do soā¦ Thatās why we need to keep looking for the very little number of articles that speaks honestly with the other contradictory point of view. Of course, I do not suggest to believe in either side, but you should read both point of views and make an educated guess (a.k.a. simulation of your future life) on what will be your life as a ML engineer in most companies.
I have read few articles which made me thinking that ML engineering role perhaps is not a happy job usually, except in a few good companies. Maybe being an Android dev (which I like too) would be a better choice, or taking a postdoc position in ML which has much less drawbacks of the ML jobs. I will pick for you the most impactful articles that changed my mind, and let me quote some of their paragraphs.
For anybody who is serious about getting ML/DL job, these articles should be read carefully. You will not find many such posts. The following are very popular posts which is suggesting how the problem is pervasive and they are not just a peculiar opinion.
Quotes:
1. Expectation does not match reality
ā¦But the truth is that data scientists typically āspend 1ā2 hours a week looking for a new jobā as stated in* this article by the Financial Times *. Furthermore, the article also states that āMachine learning specialists topped its list of developers who said they were looking for a new job, at 14.3 per cent. Data scientists were a close second, at 13.2 per cent.ā These data were collected by Stack Overflow in their survey based on 64,000 developers.
ā¦many companies hire data scientists without a suitable infrastructure in place to start getting value out of AI. This contributes to the cold start problem in AI . Couple this with the fact that these companies fail to hire senior/experienced data practitioners before hiring juniors, youāve now got a recipe for a disillusioned and unhappy relationship for both parties. The data scientist likely came in to write smart machine learning algorithms to drive insight but canāt do this because their first job is to sort out the data infrastructure and/or create analytic reports. In contrast, the company only wanted a chart that they could present in their board meeting each day. The company then get frustrated because they donāt see value being driven quickly enough and all of this leads to the data scientist being unhappy in their role.*
Another reason that data scientists are disillusioned is a similar reason to why I was disillusioned with academia : I believed that I would be able to make a huge impact on people everywhere, not just within the company. In reality, if the companyās core business is not machine learning (my previous employer is a media publishing company), itās likely that the data science that you do is only going to provide small incremental gains
2. Politics
If you seriously think that knowing lots of machine learning algorithms will make you the most valuable data scientist then go back to my first point above: **expectation does not match reality.
The truth is the people in the business with the most clout need to have a good perception of you. That may mean that you have to constantly do ad hoc work such as getting numbers from a database to give to the right people at the right time, doing simple projects just so that the right people have the right perception of you. I had to do this a lot in my previous place. As frustrating as it can feel, it was a necessary part of the job.
3) Youāre the go to person about anything data
It isnāt just non-technical executives that make too many assumptions about your skills. Other colleagues in technology assume you know everything data related. You know your way around* Spark, Hadoop, Hive, Pig, SQL, Neo4J, MySQL, Python, R, Scala, Tensorflow, A/B Testing, NLP, anything machine learning(and anything else data related that you can think of ā BTW if you see a job specification with all of these written on it, stay well clear. It reeks of a job spec from a company that has no idea what their data strategy is and theyāll hire anyone because they think that hiring any data person will fix all of their data problems).
But it doesnāt stop there. Because you know all of this and you* ***obviously have access to ALL of the data,you are expected to have the answers to ALL of the questions byā¦ā¦. well, it shouldāve landed in the relevant personās inbox 5 minutes ago.
Trying to tell everyone what you actually know and have control of can be hard. Not because anyone will actually think any less of you, but because as a junior data scientist with little industry experience youāll worry that people will think less of you. This can be quite a difficult situation.
Conclusion
So to be an effective data scientist in industry it doesnāt suffice just to do well in Kaggle competitions and complete some online courses. It (un)fortunately (depending on which way you look at it) involves understanding how hierarchies and politics works in business. Finding a company that is aligned with your critical path should be a key goal when searching for a data science job that will satisfy your needs. However, you may still need to readjust your expectations of what to expect from a data science role.
End of Quotes
Also I learned that the most important skill for a ML engineer role is computer science and algorithms. In fact Algorithms and general coding skills are the most tested skills in ML/DL job interviews, even in Google and other big names in the field. And now I think they have the right to do so:
Quotes
with the lessons below, youāll avoid many of the errors I made learning to operate on the day-to-day data science frontlines.
Production data science is mostly computer science
Data science is still highly subjective
People and communication skills are crucial
ā¦the hardest parts of data science are developing everything that occurs before and after modeling. Before we have: loading data from a database, feature engineering, data validation, and data processing pipelines (assuming our job starts after data is ingested).
Although I managed to make the mechanical engineering -> data scientist transition, in retrospect, it would have been more productive to do engineering -> computer science -> data science. The second approach would have meant I didnāt have to unlearn the poor coding practices I picked up in data science classes. In other words, I think itās easier to add data science on top of a solid computer science background than to learn data science first and then take up computer science (but both routes are possible).
Computer science involves an entirely different way of systematic thinking, methodically planning before coding, writing code slowly, and testing code once itās written. Clean code is in stark contrast to the often free-wheeling nature of data science with dozens of half-written notebooks (weāve all had notebooks called* Untitled12.ipynb ) and an emphasis on getting immediate results rather than writing rather error-free code that can be re-used.
Mess around with some completely useless proof-of-concept in Jupyter Notebook that will never see the light of day in production.
Go for a good, long lunch.
Hang around the coffee machine and discuss the strengths and weaknesses of deep learning frameworks youāve never used beyond tutorials.
Participate in random strategy meeting.
Continue on the notebook.
Try to fix your Python environment, which is in dependency hell after you installed some package that youāll never use.
Give up and surf job boards instead.
Go home.
My conclusions
I still think working as a Deep Learning engineer is the most terrific job that I can dream of, providing the company is data driven and understands what is such role is. But it seems most companies will not give you the appropriate environment and work expectations, that are needed to make the job looking attractive to me. However, there are certainly big companies that are mainly datascience oriented, they understand all the pitfalls above, like Google, Microsoft, Uber ā¦etc. And I would definitely happy to work their, however jobs at those companies are not easy to getā¦ My takeaway is perhaps it is better to work in an Android dev job which I love too, and keeping myself up to date (as a hobby) in the field of ML/DLā¦ Maybe someday I can get a job in an awesome company that understands what is ML after allā¦ I will keep try but selectivelyā¦ Of course, I am open for a postdoc in ML/DL too, since I think there are more flexibility as a postdoc and most of the problems above are not there (I am aware that it has other issues which are in academia as a general, which I think they are easier to manage than a job in ML in a company that is not data driven).
Exactly, this is what I think. First, because the job market is way larger for mobile app or web app dev jobs. Second because this will give you the necessary experience in computer science as general that cannot be transferred (unfortunately) through MOOCS which I think it has its own term:
Tacit Knowledge
Peer review of your code and getting constant and daily feedback from other great developers in a company are something, I think, will never be replaced by anything else. Thatās why it seems almost every great developer started as an employee in some point in her/his life. Even the great Jeremy worked in McKinsey for 8 years, which definitely gave him experience for skills that maybe cannot be acquired by self learning. Of course, I cannot speak about Jeremyās experience, but it seems this is always the job history of all great developers that I know.
Apologies for the long post, but I think it is very important to speak honestly on such an important topic, especially when there are very little number of people revealing their different point of view publicly than what you find usually, for some reason or another.
I donāt have hope. I am driven. I opened my first DL video one year ago and I have been doing DL since then.
I started with just one hour of video per week.
Then I took my first paid Kaggle competition.
Then I increased my hours to 8.
Then I proposed my manager that I can add value by doing 10% part time for Data Science team project.
Then I proposed it to be 50%.
Then I proposed to be a full time member.
Then I was fired.
Then I was doing Kaggle competitions full time.
Then I started searching for a Deep Learning project on Upwork.
It could be a long story, but one thing I know for sure - I will be doing Deep Learning no matter what, until I find another most obvious thing.
I wasnāt born with anything and I didnāt have passion to anything. I created my passion, my drive and thatās how I am creating my life and myself.
Between them and the replies on Twitter I have certainly learned a lot, a lot of food for thought. It is also interesting how similar our experience is, I completely hear you on the points that you raise.
Still not sure what the chances of making that happen are, but fingers crossed for your journey!
100% agree on the degree still having a lot of weight. Same with past employment in a role, which is hard to be able to demonstrate when you are trying to do something new professionally, something you havenāt done before.
but all the stories shared definitely provide a lot of food for thought.
I definitely know way more now than I did two days ago, also a lot to think about for meā¦
to provide an entirely new kind of dataset of the earth that todayās satellites arenāt capable of.
) since he has no prior background in CS and ML, heās in his thirties and was able to land on successful freelancing missions in DL.