Knowing 'a little bit about everything' is one thing, 'transitioning' to AI/ML is completely different, in this case i would say that for transition you do need to diverge. If the person who asked the questions already is a good software engineer, who actually wants to keep polishing software engineering skills and general coding, and also have NO desire to understand ML and AI, should this person prefer AI/ML? This is a genuine question, i always say that i'm not that into AI/ML, everyone thinks i'm crazy because it's such a incredible topic, so, should i study and focus on ML/AI even tough i'm not that interested, just because it's a hot topic?
Hard to say. In the last 10 years most big things in dev/tech have been about hype trains that are mainly beneficial to companies not individuals. As it was with crypto a few years back, now it's about AI. It will fade/pop at some point. It would.have been good for you if you were already in the field. Getting into it now... is kinda risky as you don't know for how long the bubble lasts.
That’s exactly my situation. Almost 20 years experience but AI/ML really doesn’t interest me enough that I’d want to spend much time on it. I think understanding it at a high level is probably useful but I have no intention of going deeper and I don’t think you need to if you’re not interested. I think it depends what direction you want to go with your career and it’s definitely not necessary for a large amount of people.
My guess is adapting AI tools will be a necessity, but maybe not learning more than the "usage" parts of ML/AI. Like you know enough about a framework to efficiently use and it (and hopefully debug too...) but not the full internals. For a lot of tasks, it'll just be another API to call. Actually product developers might have to adjust even more, as they'll have to be able to design and experiment based on data and ML capabilities.
@@pieflies there's plenty of software engineering to be done around the actual ML models - data pipelines, automated training and evaluation of models, API for output which is a combination of model output and logic etc. So in contrast to "ignoring" or switching to ML, this could be a third way where software engineering is still at the core of the role. It's where I'm at about 3Y into the ML space coming from dev and product roles.
@@kishanhajari9231 coreML is just an aspect of iOS tools. My plan is to learn how to use Tensorflow and other machine learning tools. I recently found out about Swift for tensorflow
The field is ever-expanding and somehow we're supposed to try to stay on top of EVERYTHING. Like now we're supposed to be able to know front-end, back-end, mobile, databases, security, devops, data engineering, AI etc... It's not possible.
@@ksaweryglab I guess there is that golden age for every career and he was born in that. The new generation gets treated with extremely high level applications and when it comes for them to CREATE those applications, there is an extremely overwhelming amount of information and knowledge.
@@ksaweryglab we make 6 figures for a reason, the general consensus is that we have a skills that are hard to come by so we are compensated for those skills. If you want to be at the top of your field you have to be a life long learner and gain even more/rarer skills. Tech is a fast moving industry so the people who work in it are expected to also move fast. You should love what you do and be excited to learn new things to be at the top of any field, however if you want to be a 9-5 run of the mill developer (which is fine, you might have kids, other hobbies you're passionate about etc. Average at your job probably just means you're a more well rounded individual, unlike someone like me who codes till 9am-7/9pm some days goes to sleep and does it all over again, I'm young and frankly view most of this stuff as hobby That being said I think there are alot of places that prefer someone who's an expert at one thing rather than a jack of all trades. If your boss ever says "We're letting people go because we only want frontend engineers who know AI, I'll gladly eat my words.
@@pb8655 why shouldn't AI do frontend, at least the visible parts which are easy to verify for a product designer? In the longer run, empowering the product ppl to envision, steer & verify the created software products seems desirable, with maybe only a QA, architecture, security tech role left to verify AI created code from the technical side. But we'll see how it all plays out! Regarding specialist/generalist I don't know how the balance between these will change, interesting question! Knowing the exact syntaxes etc. might get less important, but knowing the depths and nuances to judge where subtle errors or vulnerabilities lie probably continues to be vital for crucial systems. I myself am a generalist, 17Y in IT roles, recent 3Y in ML as software dev. Still on the fence on how deep I want/should delve into the data science particulars, stay on dev side with MLOps or also take on solution architect stuff within ML. Options, options... :-)
Bro I'm choosing Computer engineering with AIML in it but i want to go for the Computer science with IOT which one should i got for better scope jobs???
Sir It amazes me to hear someone speak with such a perspective. But I also wonder whether today’s generation has the ears to understand and comprehend this gem of a perspective.🙏🙏🙏🙏
Very positive and realistic answer. Great
Knowing 'a little bit about everything' is one thing, 'transitioning' to AI/ML is completely different, in this case i would say that for transition you do need to diverge. If the person who asked the questions already is a good software engineer, who actually wants to keep polishing software engineering skills and general coding, and also have NO desire to understand ML and AI, should this person prefer AI/ML?
This is a genuine question, i always say that i'm not that into AI/ML, everyone thinks i'm crazy because it's such a incredible topic, so, should i study and focus on ML/AI even tough i'm not that interested, just because it's a hot topic?
this ^
Hard to say. In the last 10 years most big things in dev/tech have been about hype trains that are mainly beneficial to companies not individuals.
As it was with crypto a few years back, now it's about AI. It will fade/pop at some point.
It would.have been good for you if you were already in the field.
Getting into it now... is kinda risky as you don't know for how long the bubble lasts.
That’s exactly my situation. Almost 20 years experience but AI/ML really doesn’t interest me enough that I’d want to spend much time on it.
I think understanding it at a high level is probably useful but I have no intention of going deeper and I don’t think you need to if you’re not interested.
I think it depends what direction you want to go with your career and it’s definitely not necessary for a large amount of people.
My guess is adapting AI tools will be a necessity, but maybe not learning more than the "usage" parts of ML/AI. Like you know enough about a framework to efficiently use and it (and hopefully debug too...) but not the full internals. For a lot of tasks, it'll just be another API to call. Actually product developers might have to adjust even more, as they'll have to be able to design and experiment based on data and ML capabilities.
@@pieflies there's plenty of software engineering to be done around the actual ML models - data pipelines, automated training and evaluation of models, API for output which is a combination of model output and logic etc.
So in contrast to "ignoring" or switching to ML, this could be a third way where software engineering is still at the core of the role. It's where I'm at about 3Y into the ML space coming from dev and product roles.
Thanks a lot Uttsav for responding to my query - it changed my perspective on ML learning :)
I've been thinking about this too, I am an iOS developer. My conclusion was to learn AI/ML and integrate it into iOS mobile apps.
This
What was your learning?
Are you learn about coreML in ios ?
@@kishanhajari9231 coreML is just an aspect of iOS tools. My plan is to learn how to use Tensorflow and other machine learning tools. I recently found out about Swift for tensorflow
I’ll just learn both
The field is ever-expanding and somehow we're supposed to try to stay on top of EVERYTHING. Like now we're supposed to be able to know front-end, back-end, mobile, databases, security, devops, data engineering, AI etc... It's not possible.
@@ksaweryglab I guess there is that golden age for every career and he was born in that. The new generation gets treated with extremely high level applications and when it comes for them to CREATE those applications, there is an extremely overwhelming amount of information and knowledge.
@@ksaweryglab we make 6 figures for a reason, the general consensus is that we have a skills that are hard to come by so we are compensated for those skills.
If you want to be at the top of your field you have to be a life long learner and gain even more/rarer skills. Tech is a fast moving industry so the people who work in it are expected to also move fast.
You should love what you do and be excited to learn new things to be at the top of any field, however if you want to be a 9-5 run of the mill developer (which is fine, you might have kids, other hobbies you're passionate about etc. Average at your job probably just means you're a more well rounded individual, unlike someone like me who codes till 9am-7/9pm some days goes to sleep and does it all over again, I'm young and frankly view most of this stuff as hobby
That being said I think there are alot of places that prefer someone who's an expert at one thing rather than a jack of all trades. If your boss ever says "We're letting people go because we only want frontend engineers who know AI, I'll gladly eat my words.
@@pb8655 why shouldn't AI do frontend, at least the visible parts which are easy to verify for a product designer? In the longer run, empowering the product ppl to envision, steer & verify the created software products seems desirable, with maybe only a QA, architecture, security tech role left to verify AI created code from the technical side. But we'll see how it all plays out!
Regarding specialist/generalist I don't know how the balance between these will change, interesting question! Knowing the exact syntaxes etc. might get less important, but knowing the depths and nuances to judge where subtle errors or vulnerabilities lie probably continues to be vital for crucial systems. I myself am a generalist, 17Y in IT roles, recent 3Y in ML as software dev. Still on the fence on how deep I want/should delve into the data science particulars, stay on dev side with MLOps or also take on solution architect stuff within ML. Options, options... :-)
I am leaving my comment here so i can watch later
Bro I'm choosing Computer engineering with AIML in it but i want to go for the Computer science with IOT which one should i got for better scope jobs???
Computer science
So machine learning is not included in software development study ?
Sir
It amazes me to hear someone speak with such a perspective. But I also wonder whether today’s generation has the ears to understand and comprehend this gem of a perspective.🙏🙏🙏🙏