9 ms·

Ask HN: How to Break into AI Engineering

131 points by dragonmouse 3y ago

What are some great resources for learning the skills and knowledge necessary to start a career as an AI engineer? Gracias!

usgroup 3y ago
I think studying what happened to the “data engineer” role is a good indicator. There was a brief moment in time wherein data engineers were computer scientists specialised in distributed systems and data processing algorithms on commodity hardware. You had to know a lot on average. Then came commoditisation via the big vendors and now you really don’t need to know very much. As a result it is not uncommon to meet “senior” data engineers who mostly script Python, do SQL and configure Airflow. I think ML ala AI has already gone that way and many vendors are strongly promoting developer participation with courses and plug-and-play resources. So what do you need? A vendor certificate you took over a weekend, and an employer to say “yes”…
- jstx1 3y ago
  > an employer to say “yes”… This is a "just draw the owl" kind of comment. It's hard to get hired with just a certificate for plug and play tools and it isn't the kind of job OP wants anyway.
  - usgroup 3y ago
    I wasn’t commenting on how difficult it is to be hired —- that’s relative to many things. But in terms of the least you need, I think I’m unfortunately right because I have seen it many times over the least few years. We will see over the next few years when every 2nd BI analyst has become a AI engineer :-)
nanciewell 3y ago
[flagged]
the_burner_acct 3y ago
https://a16z.com/2023/05/25/ai-canon/ https://a16z.com/2023/05/25/ai-canon/ Covers a lot of ground
nikhizzle 3y ago
Assuming you have the math and algorithmic background, I would start by reading the “attention is all you need” paper. After reading, attempt to build a baby transformer model in PyTorch. After that, consider constructing some of the building blocks without libraries to understand how they work.
- dragonmouse 3y ago
  And would you consider this resource to contain "the math and algorithmic background" necessary? Or is it overkill/missing some things? https://www.freecodecamp.org/news/all-the-math-you-need-in-artificial-intelligence/ https://www.freecodecamp.org/news/all-the-math-you-need-in-a...
  - Exuma 3y ago
    I'm not qualified to answer this, but I would state nothing is really "overkill" I've been "filling in the gaps" in math for almost a year now to learn machine learning stuff casually. I don't even need to use it, I am just obsessed with learning and I read about it for nearly an hour a day, and its still not enough. Being self-taught at math introduces so many painful problems. If I were to do this seriously I would start ALLLL the way back at algrebra in 5th grade and work forwards ALLL the way up to linear algebra/calculus etc. There's just too many tiny things and subtle details that are missed that I find. It makes any example require 10 times more brain power just to do simple things I don't remember, like the rules of factorization etc. So I'll go learn that thing which is simple, go back and 2 mins later I'm off trying to find some other simple thing. Mainly the idea of learning 5th grade math and such is so boring I just never have actually done it, so really instead of what I do, I would just learn the ENTIRE freaking thing. Learning stuff like gradient descent and stuff is easy, but that's not even where all the hard stuff lies. I feel like trying to understand the math deeply behind those topics where youre not just glossing over explanation is where it gets difficult, and to do that you pretty much need the most solid math background without gaps.
    - bick_nyers 3y ago
      If you have a good handle on undergraduate math material I highly recommend graduate math material. You will "restart" math in a sense and start building up those building blocks piece by piece. If you have a shaky understanding of a derivative that's fine because you will do epsilon-delta proofs until your eyes bleed in a Real Analysis course. Edit: I didn't have the laws/properties of logarithms memorized/understood until maybe 3-4 years into my Math degree. I could have learned it sooner probably, but I just had an aversion to it and would desperately translate any problem into exponentials and work with those instead. I definitely sympathise with the desire to "restart" Math.
71a54xd 3y ago
Has anyone here transitioned into an ai dev adjacent role, akin to something a bit more involved than "prompt engineering" potentially the "product eng" equivalent of AI?
- itake 3y ago
  I moved into content moderation, which basically does all the engineering around ML models (logging, queuing, caching, databases, etc.) with limited opportunities to create my own models. Unfortunately, the barrier to entry is high, and it has been difficult for me to find the time to step out of my comfort zone.
- electrondood 3y ago
  That's me, currently. At a small startup and no one else seemed to be as interested in LLMs, so they made me the guy. Still a dev, deploying various models, integrating with our services, instrumentation, prompt management, etc. Basically architecting LLM-related infrastructure to enable the product features they want, while managing expectations.
ukuina 3y ago
I would recommend AI Applications Engineering (e.g., applying LLMs as a unit of compute). Start with reading AI Canon and setting up projects like PrivateGPT and AutoGPT locally, then working with LocalAI to serve up HuggingFace models in place of OpenAI models.
f38zf5vdt 3y ago
1. Log onto OpenAI 2. Prompt ChatGPT for "how do I make a pytorch program that learns to do <some task>" 3. Ask ChatGPT for code and for it to explain the code 4. Run the code on Google colab, if it doesn't work, ask ChatGPT why and keep rerunning it until it does 5. If you find some API that's too new for ChatGPT to know about, just paste in the API documentation and then ask ChatGPT to propose some code using it ChatGPT is wrong a lot, but if you keep badgering it, eventually you will get a solution that works. It's like having a tutor standing next to you that you can ask questions to, I can't think of a better way to learn even if it's wrong on occasion.
rch 3y ago
I've been talking up the idea of finally settling on a PE certification for software engineers (in the U.S.). It seems like most of the risks and responsibilities being discussed in the context of government regulations could be addressed with mechanisms similar to what we rely on for aircraft, bridges, power plants, etc. -- all those areas have processional credentialing in addition to bureaucratic oversight.
- wlesieutre 3y ago
  They tried it from 2013 to 2019 with a total of 81 candidates, and it's been discontinued for lack of interest.
  - techwizrd 3y ago
    Source for folks: https://ncees.org/ncees-discontinuing-pe-software-engineering-exam/ https://ncees.org/ncees-discontinuing-pe-software-engineerin...
    - rch 3y ago
      I'm familiar, but that definition wasn't well thought out. Other approaches have been more widely adopted, like FINRA certs for working with trading infrastructure. I'd much rather include knowledgeable individuals with professional authority in the loop than rely entirely on box checking as with various ISO standards, HIPPA, FedRAMP etc.
santiagobasulto 3y ago
AI Engineering is basically Data Engineering focused on AI. When in "traditional" Data Engineering you create pipelines that store processed data in something like a Data Lake, in AI Eng. your end storage might be a specialized Feature Storage (like Feast or GCP Vertex AI). There are some AI Engineers with strong scientific/mathematical background, but that's rare. Usually, you're paired with these ML people that actually develop and evaluate the models. So my advice is to start with Data Engineering and then find a specialization AI. You should have a VERY solid foundation on scripting and programming, specially Python. Also, a lot of concepts of "data wrangling". Understanding how data flows from point A to point B, how the intermediate storages and streaming engines work, etc. Functional programming is key here. [0] https://github.com/feast-dev/feast https://github.com/feast-dev/feast
- achileas 3y ago
  This is exactly what I did years ago, and it's much closer to software engineering than building models (which a lot of commenters are conflating with MLE - but tbf the titles aren't delineated well in practice).
- jstx1 3y ago
  > AI Engineering is basically Data Engineering focused on AI. I work in machine learning and this isn't how I see it at all. Data engineering specifically evolved as a term to differentiate the people who work with data but don't work on ML/AI.
  - achileas 3y ago
    I work in machine learning and also in data engineering, and for most of my career the data engineering title was for people doing everything in the lifecycle outside of R&D workflows (building the models/model architecture itself). It's only very recently differentiated to MLE/DE, and even that is far from being a standard. The skillset is largely the same, but with some specialized knowledge for ML data work.
tikkun 3y ago
I wrote something a bit ago to answer this question: https://llm-utils.org/AI+Learning+Curation https://llm-utils.org/AI+Learning+Curation It was previously popular on HN though only inside a comment thread, and I haven't submitted it as a link post yet.
- StevenHarlow 3y ago
  This looks like a very good resource, thank you. It also clarifies for me a bit more the domains within the field, which I wouldn't have been able to define in terms of prerequisites.
loudmax 3y ago
Check out the Huggingface NLP course: https://huggingface.co/learn/nlp-course/chapter1/1 https://huggingface.co/learn/nlp-course/chapter1/1 Huggingface has a bunch of courses, but that's a good one to start with. You can do the exercises on your own computer or on a cloud server if you want access to a more powerful GPU. If you go through these courses and pay attention you'll be in a really good position.
RcouF1uZ4gsC 3y ago
Maybe a little bit of a contrarian idea, but I would be really leery of trying to become an "AI engineer" now. There is a possibility that we are at the apex of this cycle of AI (if you look at history, you will see AI goes in cycles), and we will run into more and more limitations. Instead, of targeting AI engineering, I would focus on obtaining a solid mathematics background (calculus, linear algebra, discrete mathematics) and a solid computer science background (algorithms, data structures, distributed systems, databases/data storage/data retrieval). Then with those skills, you can easily become a "SW Engineer who leverages AI" which in my guess will be a much better job and more stable than "AI Engineer"
__rito__ 3y ago
- Have crystal clear Mathematical foundations, as in why this formula/method the way it is, rather than being able to solve college/HS test problems. Really solid footing in Differential Calculus and Linear Algebra is necessary. - Know the Statistical language that you learn from a basic college-level Stat 101 course. Be able to translate normal sentences into those using Statistical notation, and be able to read easily. Also, know basic Statistics. - You already know programming, I assume. Learn Python if you don't know already. It's really easy. - There are a number of paths you can go from there. Here's what I did. -- IBM Data Science Professional Certificate (not deep at all, but lays out the landscape well; did it in a week) -- Machine Learning for Absolute Beginners by Oliver Theobald which you can finish in an evening. -- Machine Learning Specialization by Andrew Ng on Coursera. -- Deep Learning Specialization by Andrew Ng on Coursera. -- fast.ai course. - Learn PyTorch really well. I suggest Sebastian Raschka's book. Now from here, you can chart your own path. You can choose NLProc, Vision, RL, or something else. I went towards Vision. And I do Edge AI as hobby. I was in the last year of college as a Physics undergrad, when I was hired to do Vision modelling/research for a non-flashy company in 2021. Finishing my CS Master's next month and starting to look for PhD. I worked in the same company for the ~2.5 years. EDIT: If you want a job in big tech, grind Leetcode, and learn about system design, study Machine Learning systems, and be able to design them. Chip Huyen has a good book as I hear. 6-7 rounds of interview is common in Meta/Google. DL hackathon awards, open source contributions are significantly helpful.
- jahsome 3y ago
  Highjacking for self-edification as you led with math... Is math going to become more important for software engineers in general, as AI is adopted in to more and more aspects of the SDLC, even if said engineers aren't working directly on the AI systems themselves? I ask because I'm quite math averse (dyscalulia). I am self-taught engineer, largely because it became glaringly obvious I wasn't going to be able to complete a CS program, so I pivoted my educational focus. I still manage a relatively successful and effective career in software. I love the engineering aspects, e.g. programming, system design, problem solving, pattern recognition, etc. When it comes down to it, I couldn't calculate my way out of a wet paper sack. Are my days (pun intended) numbered?
electrondood 3y ago
Can you elaborate on what "AI engineer" means to you?
- Razengan 3y ago
  Make a Terminator to smite my enemies.
- deleted 3y ago
  [deleted]
  - jstx1 3y ago
    [dead]
- dragonmouse 3y ago
  I left the question a bit open ended to see the diversity of the field and to hopefully gather advice that could benefit others as well. That said, primarily _for me_ it's more defined in what it doesn't mean - Which is software engineering _using_ AI (eg - LLMs) and prompt engineering. I'm already a self-taught web/mobile developer with 10+ years experience, and have built some toy projects using LLMs and image diffusion models. Where I believe I have more interest is in the research scientist route. I have prior education in biology/ecology/behavior, and would love to combine the fields.
  - electrondood 3y ago
    Got it, thx for clarification. I think you're asking how to move into data science.
  - gremlinsinc 3y ago
    there's ml, which requires math. lots of it, for the rest of us there's so much we can build now by just interfacing with language models using tools like langchain or llama index. The future of ai, is models so advance only an ai can understand or create them, and they will do just that I'm a feedback loop of sorts.
hackernoteng 3y ago
Why? Do you like to make software that barely works? Do you like to make and sell software which is dishonest in its capabilities? In traditional software, 10% error rate is unacceptable - it can't be released. But hey 10% error rate in AI is good! No, it isn't.
- mehh 3y ago
  You’re either missing the point, or you’ve seen too many demos of people using neural networks for the wrong problem.
markus_zhang 3y ago
I'm surprised that many recommend heavy Math, but I always thought AI engineer is sort of specialized data engineer for AI projects. It's definitely a plus to know a bit about the Mathematics, but I doubt anything short of a Master of Science in Math with AI as specialization is going to close the gap. How many data engineers can do that? Wouldn't it make a lot of sense to hire someone with zero AI exposure but tons of experience in sysadmin, data engineering and ops? It's going to be tough to find someone who are both an engineer and a Math wizard, I think.
martingalex2 3y ago
When does the AI do the AI engineering?
thatsadude 3y ago
AI Engineering is broad. There are a lot of things to learn and a lot of mistakes you have to made yourself. For applied ML, my tips are: make sure you learn the dark side of BatchNorm and Dropout, start with simple and elegant baselines instead of complex SOTA algorithms, spend more time on understanding your data than trying algorithms, be aware that SOTA in a related task will often suck at your task, be data driven. Also, most of your ideas will not work but you have to try and conduct experiments carefully.
achileas 3y ago
Can you clarify what you mean by "AI engineering?" There are two main paths right now - this is from my experience as a software engineer overall for a decade, a data engineer of some form for all but a year and a half of that, and a DE/MLE working on AI R&D teams for the last 5.5 years. 1. MLE/DE/MLOps - this is more like typical software engineering. You're responsible for building data platforms, tools, monitoring, and more around the model development lifecycle. This can include: data ingestion, data architecture, data transformation and storage, automating and productionizing various workflows like training, evaluation, and deployment, monitoring deployed models, data monitoring (and building monitoring), tooling like feature stores (and libraries for R&D teams to interact with them) or internal deep learning frameworks, etc. You'll basically work as a part (or an adjunct to) the research team that is testing new model architectures, different approaches towards some goal, etc. These are largely taken from my own experiences and projects I've built. Skills: software engineering, Python, knowledge of the model development lifecycle, data architecture/engineering, some knowledge about the frameworks used, cloud platforms, etc. Designing ML Systems by Chip Huyen is a great overview of all of this kind of work. 2. Research. This is actually building models, implementing papers, very occasionally (especially in big companies) doing publishable research. This is more akin to academic work (my educational background is in hard science academia), and requires a lot of paper reading, experimentation, etc. It will require knowledge of your niche (I mostly work with CV teams, for instance), strong math fundamentals, and very often a PhD. I can tell you how I, as a self-taught software engineer with a bio education got here. My first job was a generic enterprise desktop application development role, randomly joined a data engineering team shortly after that not even knowing what DE was, but knowing I liked it. We worked on a massive distributed ETL system. I then joined my first startup, it was also a DE role, but we were a small group in a larger research team where I got my initial exposure to ML workflows and especially moving them to the cloud. We did some simple model training, data management, and building products around the models we built while also supporting the research efforts of the larger team. I then went to another startup, where I had the sole responsibility of our research infrastructure (largely based on the strength of my knowledge of AWS and Python). I was the sole engineer on a team of CV researchers, and did things like automate their entire evaluation workflow and move it to the cloud, worked on the internal deep learning framework, and built a team to evaluate the current AI development lifecycle and design a platform to harden and optimize the process. Covid put the kibosh on that. I moved to another, earlier startup, doing similar work but more foundational - almost everything was built from scratch.
devaler 3y ago
Learn everything you can about cleaning/standardizing datasets and the boots-on-the-ground process of labeling data and training algorithms. These are fundamental, and are often overlooked and undervalued.

131 points by dragonmouse 3y ago

What are some great resources for learning the skills and knowledge necessary to start a career as an AI engineer? Gracias!

usgroup 3y ago

I think studying what happened to the “data engineer” role is a good indicator. There was a brief moment in time wherein data engineers were computer scientists specialised in distributed systems and data processing algorithms on commodity hardware. You had to know a lot on average. Then came commoditisation via the big vendors and now you really don’t need to know very much. As a result it is not uncommon to meet “senior” data engineers who mostly script Python, do SQL and configure Airflow. I think ML ala AI has already gone that way and many vendors are strongly promoting developer participation with courses and plug-and-play resources. So what do you need? A vendor certificate you took over a weekend, and an employer to say “yes”…

jstx1 3y ago
> an employer to say “yes”… This is a "just draw the owl" kind of comment. It's hard to get hired with just a certificate for plug and play tools and it isn't the kind of job OP wants anyway.
- usgroup 3y ago
  I wasn’t commenting on how difficult it is to be hired —- that’s relative to many things. But in terms of the least you need, I think I’m unfortunately right because I have seen it many times over the least few years. We will see over the next few years when every 2nd BI analyst has become a AI engineer :-)

nanciewell 3y ago

[flagged]

the_burner_acct 3y ago

https://a16z.com/2023/05/25/ai-canon/ https://a16z.com/2023/05/25/ai-canon/ Covers a lot of ground

nikhizzle 3y ago

Assuming you have the math and algorithmic background, I would start by reading the “attention is all you need” paper. After reading, attempt to build a baby transformer model in PyTorch. After that, consider constructing some of the building blocks without libraries to understand how they work.

dragonmouse 3y ago
And would you consider this resource to contain "the math and algorithmic background" necessary? Or is it overkill/missing some things? https://www.freecodecamp.org/news/all-the-math-you-need-in-artificial-intelligence/ https://www.freecodecamp.org/news/all-the-math-you-need-in-a...
- Exuma 3y ago
  I'm not qualified to answer this, but I would state nothing is really "overkill" I've been "filling in the gaps" in math for almost a year now to learn machine learning stuff casually. I don't even need to use it, I am just obsessed with learning and I read about it for nearly an hour a day, and its still not enough. Being self-taught at math introduces so many painful problems. If I were to do this seriously I would start ALLLL the way back at algrebra in 5th grade and work forwards ALLL the way up to linear algebra/calculus etc. There's just too many tiny things and subtle details that are missed that I find. It makes any example require 10 times more brain power just to do simple things I don't remember, like the rules of factorization etc. So I'll go learn that thing which is simple, go back and 2 mins later I'm off trying to find some other simple thing. Mainly the idea of learning 5th grade math and such is so boring I just never have actually done it, so really instead of what I do, I would just learn the ENTIRE freaking thing. Learning stuff like gradient descent and stuff is easy, but that's not even where all the hard stuff lies. I feel like trying to understand the math deeply behind those topics where youre not just glossing over explanation is where it gets difficult, and to do that you pretty much need the most solid math background without gaps.
  - bick_nyers 3y ago
    If you have a good handle on undergraduate math material I highly recommend graduate math material. You will "restart" math in a sense and start building up those building blocks piece by piece. If you have a shaky understanding of a derivative that's fine because you will do epsilon-delta proofs until your eyes bleed in a Real Analysis course. Edit: I didn't have the laws/properties of logarithms memorized/understood until maybe 3-4 years into my Math degree. I could have learned it sooner probably, but I just had an aversion to it and would desperately translate any problem into exponentials and work with those instead. I definitely sympathise with the desire to "restart" Math.

71a54xd 3y ago

Has anyone here transitioned into an ai dev adjacent role, akin to something a bit more involved than "prompt engineering" potentially the "product eng" equivalent of AI?

itake 3y ago
I moved into content moderation, which basically does all the engineering around ML models (logging, queuing, caching, databases, etc.) with limited opportunities to create my own models. Unfortunately, the barrier to entry is high, and it has been difficult for me to find the time to step out of my comfort zone.
electrondood 3y ago
That's me, currently. At a small startup and no one else seemed to be as interested in LLMs, so they made me the guy. Still a dev, deploying various models, integrating with our services, instrumentation, prompt management, etc. Basically architecting LLM-related infrastructure to enable the product features they want, while managing expectations.

ukuina 3y ago

I would recommend AI Applications Engineering (e.g., applying LLMs as a unit of compute). Start with reading AI Canon and setting up projects like PrivateGPT and AutoGPT locally, then working with LocalAI to serve up HuggingFace models in place of OpenAI models.

f38zf5vdt 3y ago

1. Log onto OpenAI 2. Prompt ChatGPT for "how do I make a pytorch program that learns to do <some task>" 3. Ask ChatGPT for code and for it to explain the code 4. Run the code on Google colab, if it doesn't work, ask ChatGPT why and keep rerunning it until it does 5. If you find some API that's too new for ChatGPT to know about, just paste in the API documentation and then ask ChatGPT to propose some code using it ChatGPT is wrong a lot, but if you keep badgering it, eventually you will get a solution that works. It's like having a tutor standing next to you that you can ask questions to, I can't think of a better way to learn even if it's wrong on occasion.

rch 3y ago

I've been talking up the idea of finally settling on a PE certification for software engineers (in the U.S.). It seems like most of the risks and responsibilities being discussed in the context of government regulations could be addressed with mechanisms similar to what we rely on for aircraft, bridges, power plants, etc. -- all those areas have processional credentialing in addition to bureaucratic oversight.

wlesieutre 3y ago
They tried it from 2013 to 2019 with a total of 81 candidates, and it's been discontinued for lack of interest.
- techwizrd 3y ago
  Source for folks: https://ncees.org/ncees-discontinuing-pe-software-engineering-exam/ https://ncees.org/ncees-discontinuing-pe-software-engineerin...
  - rch 3y ago
    I'm familiar, but that definition wasn't well thought out. Other approaches have been more widely adopted, like FINRA certs for working with trading infrastructure. I'd much rather include knowledgeable individuals with professional authority in the loop than rely entirely on box checking as with various ISO standards, HIPPA, FedRAMP etc.

santiagobasulto 3y ago

AI Engineering is basically Data Engineering focused on AI. When in "traditional" Data Engineering you create pipelines that store processed data in something like a Data Lake, in AI Eng. your end storage might be a specialized Feature Storage (like Feast or GCP Vertex AI). There are some AI Engineers with strong scientific/mathematical background, but that's rare. Usually, you're paired with these ML people that actually develop and evaluate the models. So my advice is to start with Data Engineering and then find a specialization AI. You should have a VERY solid foundation on scripting and programming, specially Python. Also, a lot of concepts of "data wrangling". Understanding how data flows from point A to point B, how the intermediate storages and streaming engines work, etc. Functional programming is key here. [0] https://github.com/feast-dev/feast https://github.com/feast-dev/feast

achileas 3y ago
This is exactly what I did years ago, and it's much closer to software engineering than building models (which a lot of commenters are conflating with MLE - but tbf the titles aren't delineated well in practice).
jstx1 3y ago
> AI Engineering is basically Data Engineering focused on AI. I work in machine learning and this isn't how I see it at all. Data engineering specifically evolved as a term to differentiate the people who work with data but don't work on ML/AI.
- achileas 3y ago
  I work in machine learning and also in data engineering, and for most of my career the data engineering title was for people doing everything in the lifecycle outside of R&D workflows (building the models/model architecture itself). It's only very recently differentiated to MLE/DE, and even that is far from being a standard. The skillset is largely the same, but with some specialized knowledge for ML data work.

tikkun 3y ago

I wrote something a bit ago to answer this question: https://llm-utils.org/AI+Learning+Curation https://llm-utils.org/AI+Learning+Curation It was previously popular on HN though only inside a comment thread, and I haven't submitted it as a link post yet.

StevenHarlow 3y ago
This looks like a very good resource, thank you. It also clarifies for me a bit more the domains within the field, which I wouldn't have been able to define in terms of prerequisites.

loudmax 3y ago

Check out the Huggingface NLP course: https://huggingface.co/learn/nlp-course/chapter1/1 https://huggingface.co/learn/nlp-course/chapter1/1 Huggingface has a bunch of courses, but that's a good one to start with. You can do the exercises on your own computer or on a cloud server if you want access to a more powerful GPU. If you go through these courses and pay attention you'll be in a really good position.

RcouF1uZ4gsC 3y ago

Maybe a little bit of a contrarian idea, but I would be really leery of trying to become an "AI engineer" now. There is a possibility that we are at the apex of this cycle of AI (if you look at history, you will see AI goes in cycles), and we will run into more and more limitations. Instead, of targeting AI engineering, I would focus on obtaining a solid mathematics background (calculus, linear algebra, discrete mathematics) and a solid computer science background (algorithms, data structures, distributed systems, databases/data storage/data retrieval). Then with those skills, you can easily become a "SW Engineer who leverages AI" which in my guess will be a much better job and more stable than "AI Engineer"

__rito__ 3y ago

- Have crystal clear Mathematical foundations, as in why this formula/method the way it is, rather than being able to solve college/HS test problems. Really solid footing in Differential Calculus and Linear Algebra is necessary. - Know the Statistical language that you learn from a basic college-level Stat 101 course. Be able to translate normal sentences into those using Statistical notation, and be able to read easily. Also, know basic Statistics. - You already know programming, I assume. Learn Python if you don't know already. It's really easy. - There are a number of paths you can go from there. Here's what I did. -- IBM Data Science Professional Certificate (not deep at all, but lays out the landscape well; did it in a week) -- Machine Learning for Absolute Beginners by Oliver Theobald which you can finish in an evening. -- Machine Learning Specialization by Andrew Ng on Coursera. -- Deep Learning Specialization by Andrew Ng on Coursera. -- fast.ai course. - Learn PyTorch really well. I suggest Sebastian Raschka's book. Now from here, you can chart your own path. You can choose NLProc, Vision, RL, or something else. I went towards Vision. And I do Edge AI as hobby. I was in the last year of college as a Physics undergrad, when I was hired to do Vision modelling/research for a non-flashy company in 2021. Finishing my CS Master's next month and starting to look for PhD. I worked in the same company for the ~2.5 years. EDIT: If you want a job in big tech, grind Leetcode, and learn about system design, study Machine Learning systems, and be able to design them. Chip Huyen has a good book as I hear. 6-7 rounds of interview is common in Meta/Google. DL hackathon awards, open source contributions are significantly helpful.

jahsome 3y ago
Highjacking for self-edification as you led with math... Is math going to become more important for software engineers in general, as AI is adopted in to more and more aspects of the SDLC, even if said engineers aren't working directly on the AI systems themselves? I ask because I'm quite math averse (dyscalulia). I am self-taught engineer, largely because it became glaringly obvious I wasn't going to be able to complete a CS program, so I pivoted my educational focus. I still manage a relatively successful and effective career in software. I love the engineering aspects, e.g. programming, system design, problem solving, pattern recognition, etc. When it comes down to it, I couldn't calculate my way out of a wet paper sack. Are my days (pun intended) numbered?

electrondood 3y ago

Can you elaborate on what "AI engineer" means to you?

Razengan 3y ago
Make a Terminator to smite my enemies.
deleted 3y ago
[deleted]
- jstx1 3y ago
  [dead]
dragonmouse 3y ago
I left the question a bit open ended to see the diversity of the field and to hopefully gather advice that could benefit others as well. That said, primarily _for me_ it's more defined in what it doesn't mean - Which is software engineering _using_ AI (eg - LLMs) and prompt engineering. I'm already a self-taught web/mobile developer with 10+ years experience, and have built some toy projects using LLMs and image diffusion models. Where I believe I have more interest is in the research scientist route. I have prior education in biology/ecology/behavior, and would love to combine the fields.
- electrondood 3y ago
  Got it, thx for clarification. I think you're asking how to move into data science.
- gremlinsinc 3y ago
  there's ml, which requires math. lots of it, for the rest of us there's so much we can build now by just interfacing with language models using tools like langchain or llama index. The future of ai, is models so advance only an ai can understand or create them, and they will do just that I'm a feedback loop of sorts.

hackernoteng 3y ago

Why? Do you like to make software that barely works? Do you like to make and sell software which is dishonest in its capabilities? In traditional software, 10% error rate is unacceptable - it can't be released. But hey 10% error rate in AI is good! No, it isn't.

mehh 3y ago
You’re either missing the point, or you’ve seen too many demos of people using neural networks for the wrong problem.

markus_zhang 3y ago

I'm surprised that many recommend heavy Math, but I always thought AI engineer is sort of specialized data engineer for AI projects. It's definitely a plus to know a bit about the Mathematics, but I doubt anything short of a Master of Science in Math with AI as specialization is going to close the gap. How many data engineers can do that? Wouldn't it make a lot of sense to hire someone with zero AI exposure but tons of experience in sysadmin, data engineering and ops? It's going to be tough to find someone who are both an engineer and a Math wizard, I think.

martingalex2 3y ago

When does the AI do the AI engineering?

thatsadude 3y ago

AI Engineering is broad. There are a lot of things to learn and a lot of mistakes you have to made yourself. For applied ML, my tips are: make sure you learn the dark side of BatchNorm and Dropout, start with simple and elegant baselines instead of complex SOTA algorithms, spend more time on understanding your data than trying algorithms, be aware that SOTA in a related task will often suck at your task, be data driven. Also, most of your ideas will not work but you have to try and conduct experiments carefully.

achileas 3y ago

Can you clarify what you mean by "AI engineering?" There are two main paths right now - this is from my experience as a software engineer overall for a decade, a data engineer of some form for all but a year and a half of that, and a DE/MLE working on AI R&D teams for the last 5.5 years. 1. MLE/DE/MLOps - this is more like typical software engineering. You're responsible for building data platforms, tools, monitoring, and more around the model development lifecycle. This can include: data ingestion, data architecture, data transformation and storage, automating and productionizing various workflows like training, evaluation, and deployment, monitoring deployed models, data monitoring (and building monitoring), tooling like feature stores (and libraries for R&D teams to interact with them) or internal deep learning frameworks, etc. You'll basically work as a part (or an adjunct to) the research team that is testing new model architectures, different approaches towards some goal, etc. These are largely taken from my own experiences and projects I've built. Skills: software engineering, Python, knowledge of the model development lifecycle, data architecture/engineering, some knowledge about the frameworks used, cloud platforms, etc. Designing ML Systems by Chip Huyen is a great overview of all of this kind of work. 2. Research. This is actually building models, implementing papers, very occasionally (especially in big companies) doing publishable research. This is more akin to academic work (my educational background is in hard science academia), and requires a lot of paper reading, experimentation, etc. It will require knowledge of your niche (I mostly work with CV teams, for instance), strong math fundamentals, and very often a PhD. I can tell you how I, as a self-taught software engineer with a bio education got here. My first job was a generic enterprise desktop application development role, randomly joined a data engineering team shortly after that not even knowing what DE was, but knowing I liked it. We worked on a massive distributed ETL system. I then joined my first startup, it was also a DE role, but we were a small group in a larger research team where I got my initial exposure to ML workflows and especially moving them to the cloud. We did some simple model training, data management, and building products around the models we built while also supporting the research efforts of the larger team. I then went to another startup, where I had the sole responsibility of our research infrastructure (largely based on the strength of my knowledge of AWS and Python). I was the sole engineer on a team of CV researchers, and did things like automate their entire evaluation workflow and move it to the cloud, worked on the internal deep learning framework, and built a team to evaluate the current AI development lifecycle and design a platform to harden and optimize the process. Covid put the kibosh on that. I moved to another, earlier startup, doing similar work but more foundational - almost everything was built from scratch.

devaler 3y ago

Learn everything you can about cleaning/standardizing datasets and the boots-on-the-ground process of labeling data and training algorithms. These are fundamental, and are often overlooked and undervalued.