Hey everyone, happy to answer any questions around Data Science/ML/AI and making a career within it
A brief about me, I work currently as a Sr. ML Engineer at an Indian unicorn startup I graduated 5+ years ago and have since worked as a Data Scientist / ML Engineer at an ecommerce company and social media company. My designations have changed based on the industry requirements and work too changed based on that.
Would love to take questions on what it means to be an ML Engineer, my early learnings from a career in data science, and thoughts on AI.
Look forward to all questions :)
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thanks for doing AMA,
With seniority, part of my day goes into meetings - discussions around what is it that we are building, progress checks, brainstorming around what to build next, PRD etc. I also have the added on responsibility of a team lead, where I have to delegate work and ensure timelines are met - in other words - do standups and unblock people. I try to dedicate the rest of the time for coding purposes. Some days hacking things together in a jupyter notebook, other times deploying some change, some days integrating the ML stuff with our backends.
Again, not many people have the role I do, where I have to and can make contributions to model building, production deployment and planning. So the above stuff varies for others around me where some do more hardcore model building & analysis
On question 2,
Gen AI is truly a game changer, ChatGPT, Copilot & the likes enable everyone to ship faster. Designers around me use the Adobe beta image gen functions for their work. I have seen some backend folks generate code from tools like locify from a figma mockup.
Rewording one of the popular sentiments around the recent developments - Gen AI enables the experts to be more efficient.
How do you manage to stay upto date on the new emerging technologies and updates in the filed ? Asking as I'm currently facing that issue as a fresher(1yoe))
As a fresher graduating from college, preparing for ML roles, what specific areas should one focus on especially to crack an interview?
And how does does DSA play a role in interview?
Strangely DSA is something that is never ever useful but rated super highly in your interviews. I would recommend for freshers to continue the leetcode bullshit, and build side projects around to showcase your ML capabilities. Get on twitter and start getting inspired by indie developers hacking things together.
Any company looking at your online work would love to have you, as this separates you from the 1000s of others who also "want" to do AI and fancy shit
Ah thats a bit sad...This screening is very worthless I feel and considering the ongoing internships in hand(ML intern @Siemens, AI Product intern at a Startup) it becomes difficult to practice DSA(and frankly I don't like it I'd rather want to experiment with a new model). I've even won Smart India Hackathon'22 and also participated in many other hackathons have other hands on experiences too but just the notion that you've to go through that DSA round despite dev skills crushes me. Can't complain much , have to learn it the hard way :(
Still hopeful and working hard 💪
Thankyou.
How much value do you think a masters/PhD would hold in the space. I've been working in the space for a couple of years and am a little confused on how to proceed
I personally would do what Thiel does as soon as I get that rich. Which is to sponsor people to drop out from the colleges/universities. Because they are truly waste of your time if your ambition or goal is something else / bigger.
By goal - if your goal is to do heavy research then yes, phd & masters is the way, you can get yourself into google and the likes and get something massive out. But if your goal is to be the data scientist at udaan, I don't think you need masters or phd for that
Having said that I can see why an employer would prefer a masters & phd candidate over a undergrad with some experience.
What if the person wants to get the international exposure but can't due to not being from tier 1&2 colleges
What do you make of the current generative AI wave? Is it a bubble or is it the future?
Definetly a wave, in the context that many companies will come and get popular and die right away.
But the difference this time is, Gen AI is backed into everything now, because it is that good & useful.
Gen AI for images in Adobe, the features around text generation in grammerly, docs, notion (everywhere).
I do not believe in no code BS tools, and I do not beleive anyone saying write a text and get a website out, this is not how things work at any minimal scale, but a developer can now do work that a team of developers could do a year ago.
Same goes for every other profession that has these advances.
Thanks for that reply @diffusedbandit; glad that your views echo Curious: So far in your role have there been any changes? Or is it mainly other functions that are getting better because of easier text/image/video generation
For what's it worth, does MS in Data Science or applied ML hold weightage in today's market? Quick background - fresher in industry, preparing for Masters this fall - conflicted between distributed systems and data science roles.
what tech stack are you currently using for ai developement
Everything. My role has me prototype faster. So huggingface abstractions wherever available, tensorflow serving / torch serve (yes that too) for things like encoder model deployments. For usecases with user data (read big data), PySpark.
I have not seen diversity around what thing people use around me. At the end of the day it is still python and some python packages. And then you go to prod, where things change
Are mostly complex models use or is it more depending on task. How much learning -> Execution takes place
say a user modelling problem, like recommending posts to a user on grapevine - one could make a case that simple recency + affinity based models could work. you could take up LR next to solve it and so on.
then say a text classification task, moderating say grapevine posts, hypothetically, I could see using an LLM based prompt engineering based classification model.
Companies in bangalore I do not see anyone sponsoring your pay to make a foundation model, they need you to execute faster, that usually implies that you won't learn as much but rather apply more and more
Nice answer. I think mostly execution is needed and accuracy up to +30% Works too, as compared to linear models.
Have you never thought of going to Algorithmic Trading side ? Higher pay is there
What’s the expected salary for a ~10 year Data science lead ?
I have not met many 10 year DS folks. But this question is quite difficult to answer.
Companies like sharechat or walmart have different bands, companies like Infosys have different bands. You might work on the same tech but it really depends where you work.
For a product based company , what one can expect ,? A range could help
2022 graduate here, working as a software engineer. I feel that software engineering has less to do big in future compared to Ai/ML. Need your thoughts on what are my possibilities and pros cons on choosing different paths.
Any ML/AI engineer who can share, what does a day to day work look like for you ? Is it mostly a research based role ? Do you code regularly ?
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Are there relavent companies (more importantly projects) available here in India which could help with the growth in the field of AI?
