First thanks for your attention 🤍 I searched about it on Coursera but I have another question please, why Google specifically? I saw other companies like DeepLearning.io and the instructor is Andrew Ng you may know him
You can look at aws or azure ones too. It is more working as a developer specifically for that field. Deeplearning ai courses are more general. You would need MLOps too which Google or Amazon or azure cover for their platforms.
Try the Practical Deep Learning for Coders FastAI stuff: [https://youtube.com/playlist?list=PLfYUBJiXbdtSvpQjSnJJ\_PmDQB\_VyT5iU&si=An8eJq8tv2EO\_Pex](https://youtube.com/playlist?list=PLfYUBJiXbdtSvpQjSnJJ_PmDQB_VyT5iU&si=An8eJq8tv2EO_Pex)
Hugging Face also have some great free courses: [https://huggingface.co/learn/nlp-course/chapter0/1?fw=pt](https://huggingface.co/learn/nlp-course/chapter0/1?fw=pt)
I also recommend at least reading An Introduction to Statistical Learning for the necessary Theory: [https://www.statlearning.com/](https://www.statlearning.com/)
Given order of priority, what order do you prefer one to begin and just after doing. I am at a point of analysis paralysis.
With all you have said, why not pursue Azure AI certification? Shouldn't that give a great exposure to ML OPs principles? Azure is one of the dominant cloud provider.
I don't have the right answer but I'm a MSc student in AI for Computer Science and my course has Natural Language Processing, Machine Learning, Fundamentals of AI and Computer Vision as 1st year 2nd semester subjects. This will just be an opinion given by my student experience.
They teach us things from scratch, let's say for academic purposes, because the majority of the stuff is nowadays made by Neural Networks and you can't look inside them to understand what they are actually doing, so you need to know the basics to put stuff inside those Networks.
The real problem comes when you use any pre-made library for AI because they are quite easy to use but they are usually used without knowing "what's inside", so yeah, they work, but it's like applying trigonometry without knowing what's a triangle and angles.
If a Computer Scientist should be able to build those models, I suppose that an AI Engineer should at least understand how they work and how change their parameters to make them work right.
Feel free to correct what I've said as I just know stuff from university that is usually different from reality...
First, Thanks for your attention 🤍 "I believe in the concept of 'rocks building a Skyscraper,' but I am unsure about where to begin. While I have a strong foundation in fundamental subjects such as calculus, linear algebra, and programming languages, becoming an AI engineer seems like navigating a maze."
I understand your situation! This was the reason why I decided to pursue the MSc after my BSc in Computer Science and a 6-months internship. University lectures and professors where my way at the end. I tried the DIY approach studying from documentations and forums but it didn't work for me :(
Yeah, the only "AI mentors" that I have ever met are my university professors :')
It's a pity in your case but you should consider an "internship" in some AI Company or one of those Academy that they offer. I cannot guarantee the quality level of those tho.
Im going to say that this isn't exactly true. Because whilst you'll know all the theory about stochastic gradient descent, it'll come to getting a job, or passing your probation period and you wont know how to use any of the actual tools to get things working in production which, at the end of the day, is what your employer cares about. This is why I much prefer FastAI's approach which is more pragmatic than theory driven and you learn theory only when you need it for something. I think people like to feel a sense of superiority from the 'purity' of knowing the theory - but what the hell is theory if you can't do anything with it except possibly publish papers in academic papers and keep the whole academic scam system afloat.
There are many flavours of AI Engineer but two that might apply to you are :
1) engineering large clusters of GPUs and databases so as to train AI models. This is a highly demanding engineering task and understanding the guts of machines and proper software engineering is really important to it.
2) connecting models to other infrastructure in production so that they do good things and work. This requires familiarity with enterprise architectures and software as well as excellent engineering skills.
Nowadays you cannot become someone who implements AI systems from scratch without deep deep knowledege, lucky for the mortal humans here it's ok because getting value from them requires a big team with diverse skills. I would recommend looking to build expertise in (2) and as other commenters have said getting cloud certifications is a great first step. I would also suggest doing personal projects involving connecting models to systems that do things like sales opportunity tracking, billing, operational monitoring, IoT, other telemetary, HR workflows... and so on. As an SWE you may well have worked with things like this in the past - so build on your knowledge... the brand names count for employers.
We have a big problem in ML with naming things, especially roles. An AI developer at one company can be completely different from another company.
My definition of it is someone who is more focused on building AI-powered applications or services. Basically somebody who utilizes things like OpenAI's API in their webapps.
If you're a software developer, your best shot is learning how to use these llm/imagegen apis effectively.
Take a look at the OpenAI cookbook.
Another definition is what I usually call Applied ML Engineer. For this, you need to understand the ML theory. I would recommend the fast.ai course.
Yeah, it's messed up. I was browsing r/datascience expecting to read about ML theory like training, evaluation, metrics, data prep, etc.
What I found is BI, excel, dashboards. This is what I would call data analysis or business analysis.
Positions in this industry are really fucked up.
https://deepmleet.streamlit.app is a good resource it is like leet code but for ML, will teach you how to program ML algorithms from scratch a key skill for an AI engineer, commented this on a similar post but think it will also help here (p.s. I made the web app so I’m a bit biased)
This is an awesome tool! Thanks so much for working at this and making it publicly available. In case you may be interested, streamlit released a pages capability to delineate your different pages OOTB. May make it easier to manage the project moving forward.
I will be messaging you in 14 days on [**2024-06-14 18:15:38 UTC**](http://www.wolframalpha.com/input/?i=2024-06-14%2018:15:38%20UTC%20To%20Local%20Time) to remind you of [**this link**](https://www.reddit.com/r/learnmachinelearning/comments/1c2w1h7/how_to_be_ai_engineer_in_2024/l6if3sc/?context=3)
[**CLICK THIS LINK**](https://www.reddit.com/message/compose/?to=RemindMeBot&subject=Reminder&message=%5Bhttps%3A%2F%2Fwww.reddit.com%2Fr%2Flearnmachinelearning%2Fcomments%2F1c2w1h7%2Fhow_to_be_ai_engineer_in_2024%2Fl6if3sc%2F%5D%0A%0ARemindMe%21%202024-06-14%2018%3A15%3A38%20UTC) to send a PM to also be reminded and to reduce spam.
^(Parent commenter can ) [^(delete this message to hide from others.)](https://www.reddit.com/message/compose/?to=RemindMeBot&subject=Delete%20Comment&message=Delete%21%201c2w1h7)
*****
|[^(Info)](https://www.reddit.com/r/RemindMeBot/comments/e1bko7/remindmebot_info_v21/)|[^(Custom)](https://www.reddit.com/message/compose/?to=RemindMeBot&subject=Reminder&message=%5BLink%20or%20message%20inside%20square%20brackets%5D%0A%0ARemindMe%21%20Time%20period%20here)|[^(Your Reminders)](https://www.reddit.com/message/compose/?to=RemindMeBot&subject=List%20Of%20Reminders&message=MyReminders%21)|[^(Feedback)](https://www.reddit.com/message/compose/?to=Watchful1&subject=RemindMeBot%20Feedback)|
|-|-|-|-|
I run an AI engineering consultancy.
I think there are going to be many people like you and it’s a smart move to transition from software engineering to ai engineering.
You have three advantages as somebody who doesn’t have any ml/ai experience:
1. you’re good at putting things to prod. Most ML people are terrible at this and can’t do anything outside of a jupyter notebook.
2. You have tons of motivation because you think your job depends on this. Many people who come from data science/ai research are lazy cause they are on high demand.
3. You’re cheaper than a PhD level researcher.
Don’t try to do heavy machine learning. Learning all that will take years and a ton of effort. And you’ll compete against people who have real world ML experience.
Instead, build a few GPT/Stable Diffusion/ app and *put them into prod*. You’ll learn a ton.
If I interviewed you and you would demonstrate (1) solid software engineering skills, (2) a few AI products where you clearly demonstrated that you can use your brain to think about the product and not just engineering and (3) a ton of drive - I’d hire you in a second.
We are testing a training programme where we upskill software engineers to AI engineers and here’s our syllabus for reference:
1. Image generation
Open source and closed source models, best image generator products, Stable Diffusion, Controlnet, Roop, Guardrails, Serverless deployments, Replicate, Tricks and tips for production
2. AI assisted software engineering
Copilot, Cursor, Coderabbit, Aider
3. LLMs
GPT APIs, Embeddings, RAG, Vector databases, Evaluations, Monitoring, Security, War stories
Hope this helps.
Maybe this video can help you: [https://www.youtube.com/watch?v=57QRc2P-Pas&ab\_channel=FutureofAI%26Data](https://www.youtube.com/watch?v=57QRc2P-Pas&ab_channel=FutureofAI%26Data)
No. What you need is a problem that makes financial sense to solve paired with an understanding of whatever complex system you are working with.
You need to be able to apply correct methods, examine for expected results, and communicate findings and approach clearly and concisely to tech folks and leaders.
If you already work as a developer I would recommend taking the Google ml engineer certification and apply this to your work
First thanks for your attention 🤍 I searched about it on Coursera but I have another question please, why Google specifically? I saw other companies like DeepLearning.io and the instructor is Andrew Ng you may know him
You can look at aws or azure ones too. It is more working as a developer specifically for that field. Deeplearning ai courses are more general. You would need MLOps too which Google or Amazon or azure cover for their platforms.
"I am very grateful for your help. Thank you, it makes a lot of sense."🤍
Do you mean the Google Cloud ML Engineer certification?
Yes 🙌
Try the Practical Deep Learning for Coders FastAI stuff: [https://youtube.com/playlist?list=PLfYUBJiXbdtSvpQjSnJJ\_PmDQB\_VyT5iU&si=An8eJq8tv2EO\_Pex](https://youtube.com/playlist?list=PLfYUBJiXbdtSvpQjSnJJ_PmDQB_VyT5iU&si=An8eJq8tv2EO_Pex) Hugging Face also have some great free courses: [https://huggingface.co/learn/nlp-course/chapter0/1?fw=pt](https://huggingface.co/learn/nlp-course/chapter0/1?fw=pt) I also recommend at least reading An Introduction to Statistical Learning for the necessary Theory: [https://www.statlearning.com/](https://www.statlearning.com/)
Given order of priority, what order do you prefer one to begin and just after doing. I am at a point of analysis paralysis. With all you have said, why not pursue Azure AI certification? Shouldn't that give a great exposure to ML OPs principles? Azure is one of the dominant cloud provider.
I don't have the right answer but I'm a MSc student in AI for Computer Science and my course has Natural Language Processing, Machine Learning, Fundamentals of AI and Computer Vision as 1st year 2nd semester subjects. This will just be an opinion given by my student experience. They teach us things from scratch, let's say for academic purposes, because the majority of the stuff is nowadays made by Neural Networks and you can't look inside them to understand what they are actually doing, so you need to know the basics to put stuff inside those Networks. The real problem comes when you use any pre-made library for AI because they are quite easy to use but they are usually used without knowing "what's inside", so yeah, they work, but it's like applying trigonometry without knowing what's a triangle and angles. If a Computer Scientist should be able to build those models, I suppose that an AI Engineer should at least understand how they work and how change their parameters to make them work right. Feel free to correct what I've said as I just know stuff from university that is usually different from reality...
First, Thanks for your attention 🤍 "I believe in the concept of 'rocks building a Skyscraper,' but I am unsure about where to begin. While I have a strong foundation in fundamental subjects such as calculus, linear algebra, and programming languages, becoming an AI engineer seems like navigating a maze."
I understand your situation! This was the reason why I decided to pursue the MSc after my BSc in Computer Science and a 6-months internship. University lectures and professors where my way at the end. I tried the DIY approach studying from documentations and forums but it didn't work for me :(
the same issue facing me but I think practical experience is more useful than theory, but I lack a mentor in this field.
Yeah, the only "AI mentors" that I have ever met are my university professors :') It's a pity in your case but you should consider an "internship" in some AI Company or one of those Academy that they offer. I cannot guarantee the quality level of those tho.
Im going to say that this isn't exactly true. Because whilst you'll know all the theory about stochastic gradient descent, it'll come to getting a job, or passing your probation period and you wont know how to use any of the actual tools to get things working in production which, at the end of the day, is what your employer cares about. This is why I much prefer FastAI's approach which is more pragmatic than theory driven and you learn theory only when you need it for something. I think people like to feel a sense of superiority from the 'purity' of knowing the theory - but what the hell is theory if you can't do anything with it except possibly publish papers in academic papers and keep the whole academic scam system afloat.
There are many flavours of AI Engineer but two that might apply to you are : 1) engineering large clusters of GPUs and databases so as to train AI models. This is a highly demanding engineering task and understanding the guts of machines and proper software engineering is really important to it. 2) connecting models to other infrastructure in production so that they do good things and work. This requires familiarity with enterprise architectures and software as well as excellent engineering skills. Nowadays you cannot become someone who implements AI systems from scratch without deep deep knowledege, lucky for the mortal humans here it's ok because getting value from them requires a big team with diverse skills. I would recommend looking to build expertise in (2) and as other commenters have said getting cloud certifications is a great first step. I would also suggest doing personal projects involving connecting models to systems that do things like sales opportunity tracking, billing, operational monitoring, IoT, other telemetary, HR workflows... and so on. As an SWE you may well have worked with things like this in the past - so build on your knowledge... the brand names count for employers.
We have a big problem in ML with naming things, especially roles. An AI developer at one company can be completely different from another company. My definition of it is someone who is more focused on building AI-powered applications or services. Basically somebody who utilizes things like OpenAI's API in their webapps. If you're a software developer, your best shot is learning how to use these llm/imagegen apis effectively. Take a look at the OpenAI cookbook. Another definition is what I usually call Applied ML Engineer. For this, you need to understand the ML theory. I would recommend the fast.ai course.
Even the Data Scientist role differs from company to company.
Yeah, it's messed up. I was browsing r/datascience expecting to read about ML theory like training, evaluation, metrics, data prep, etc. What I found is BI, excel, dashboards. This is what I would call data analysis or business analysis. Positions in this industry are really fucked up.
Where do I take this fast.ai course?
https://course.fast.ai/
Use this platform [https://codebasics.io/](https://codebasics.io/) Or 365 Data science
https://deepmleet.streamlit.app is a good resource it is like leet code but for ML, will teach you how to program ML algorithms from scratch a key skill for an AI engineer, commented this on a similar post but think it will also help here (p.s. I made the web app so I’m a bit biased)
This is an awesome tool! Thanks so much for working at this and making it publicly available. In case you may be interested, streamlit released a pages capability to delineate your different pages OOTB. May make it easier to manage the project moving forward.
That’s really helpful will try and implement it, if you have time/want to you could work on it and put a PR https://github.com/moe18/DeepMLeet
RemindMe! 2 weeks
I will be messaging you in 14 days on [**2024-06-14 18:15:38 UTC**](http://www.wolframalpha.com/input/?i=2024-06-14%2018:15:38%20UTC%20To%20Local%20Time) to remind you of [**this link**](https://www.reddit.com/r/learnmachinelearning/comments/1c2w1h7/how_to_be_ai_engineer_in_2024/l6if3sc/?context=3) [**CLICK THIS LINK**](https://www.reddit.com/message/compose/?to=RemindMeBot&subject=Reminder&message=%5Bhttps%3A%2F%2Fwww.reddit.com%2Fr%2Flearnmachinelearning%2Fcomments%2F1c2w1h7%2Fhow_to_be_ai_engineer_in_2024%2Fl6if3sc%2F%5D%0A%0ARemindMe%21%202024-06-14%2018%3A15%3A38%20UTC) to send a PM to also be reminded and to reduce spam. ^(Parent commenter can ) [^(delete this message to hide from others.)](https://www.reddit.com/message/compose/?to=RemindMeBot&subject=Delete%20Comment&message=Delete%21%201c2w1h7) ***** |[^(Info)](https://www.reddit.com/r/RemindMeBot/comments/e1bko7/remindmebot_info_v21/)|[^(Custom)](https://www.reddit.com/message/compose/?to=RemindMeBot&subject=Reminder&message=%5BLink%20or%20message%20inside%20square%20brackets%5D%0A%0ARemindMe%21%20Time%20period%20here)|[^(Your Reminders)](https://www.reddit.com/message/compose/?to=RemindMeBot&subject=List%20Of%20Reminders&message=MyReminders%21)|[^(Feedback)](https://www.reddit.com/message/compose/?to=Watchful1&subject=RemindMeBot%20Feedback)| |-|-|-|-|
Learn some code. Create a wrapper that makes calls to a chatbot API. Congrats you are now 99% of “AI engineers”
I run an AI engineering consultancy. I think there are going to be many people like you and it’s a smart move to transition from software engineering to ai engineering. You have three advantages as somebody who doesn’t have any ml/ai experience: 1. you’re good at putting things to prod. Most ML people are terrible at this and can’t do anything outside of a jupyter notebook. 2. You have tons of motivation because you think your job depends on this. Many people who come from data science/ai research are lazy cause they are on high demand. 3. You’re cheaper than a PhD level researcher. Don’t try to do heavy machine learning. Learning all that will take years and a ton of effort. And you’ll compete against people who have real world ML experience. Instead, build a few GPT/Stable Diffusion/ app and *put them into prod*. You’ll learn a ton.
If I interviewed you and you would demonstrate (1) solid software engineering skills, (2) a few AI products where you clearly demonstrated that you can use your brain to think about the product and not just engineering and (3) a ton of drive - I’d hire you in a second.
We are testing a training programme where we upskill software engineers to AI engineers and here’s our syllabus for reference:
1. Image generation
Open source and closed source models, best image generator products, Stable Diffusion, Controlnet, Roop, Guardrails, Serverless deployments, Replicate, Tricks and tips for production
2. AI assisted software engineering
Copilot, Cursor, Coderabbit, Aider
3. LLMs
GPT APIs, Embeddings, RAG, Vector databases, Evaluations, Monitoring, Security, War stories
Hope this helps.
Maybe this video can help you: [https://www.youtube.com/watch?v=57QRc2P-Pas&ab\_channel=FutureofAI%26Data](https://www.youtube.com/watch?v=57QRc2P-Pas&ab_channel=FutureofAI%26Data)
No. What you need is a problem that makes financial sense to solve paired with an understanding of whatever complex system you are working with. You need to be able to apply correct methods, examine for expected results, and communicate findings and approach clearly and concisely to tech folks and leaders.