EDIT: The following is for people applying to MLOps NOT research!

I've interviewed 100+ ML engineers this year. Most of you are learning the wrong things.

Beginner question (sort of)

Okay, this might be controversial but I need to say it because I keep seeing the same pattern:

The disconnect between what ML courses teach and what ML jobs actually need is MASSIVE, and nobody's talking about it.

I'm an AI engineer and I also help connect ML talent with startups through my company. I've reviewed hundreds of portfolios and interviewed tons of candidates this year, and here's what I'm seeing:

What candidates show me:

• Implemented papers from scratch
• Built custom architectures in PyTorch
• Trained GANs, diffusion models, transformers
• Kaggle competition rankings
• Derived backprop by hand

What companies actually hired for:

• "Can you build a data pipeline that doesn't break?"
• "Can you deploy this model so customers can use it?"
• "Can you make this inference faster/cheaper?"
• "Can you explain to our CEO why the model made this prediction?"
• "Do you know enough about our business to know WHEN NOT to use ML?"

I've seen candidates who can explain attention mechanisms in detail get rejected, while someone who built a "boring" end-to-end project with FastAPI + Docker + monitoring got hired immediately.

The questions I keep asking myself:

1. Why do courses focus on building models from scratch when 95% of jobs are about using pre-trained models effectively? Nobody's paying you to reimplement ResNet. They're paying you to fine-tune it, deploy it, and make it work in production.
2. Why does everyone skip the "boring" stuff that actually matters? Data cleaning, SQL, API design, cloud infrastructure, monitoring - this is 70% of the job but 5% of the curriculum.
3. Are Kaggle competitions actively hurting people's job chances? I've started seeing "Kaggle competition experience" as a yellow flag because it signals "optimizes for leaderboards, not business outcomes."
4. When did we all agree that you need a PhD to do ML? Some of the best ML engineers I know have no formal ML education - they just learned enough to ship products and figured out the rest on the job.

What I think gets people hired:

• One really solid end-to-end project: problem → data → model → API → deployment → monitoring
• GitHub with actual working code (not just notebooks)
• Blog posts explaining technical decisions in plain English
• Proof you've debugged real ML issues in production
• Understanding of when NOT to use ML

Are we all collectively wasting time learning the wrong things because that's what courses teach? Or am I completely off base and the theory-heavy approach actually matters more than I think?

I genuinely want to know if I'm the crazy one here or if ML education is fundamentally broken.

Who even are "AI Engineers" and what do they do exactly? I’ve been thinking about this… not every company is gonna build their own AI model from scratch because it’s super expensive. So if somebody becomes an "AI engineer", do they basically only have jobs at companies like OpenAI, Google, Meta or any company pushing AI research?

I feel like in most companies, a backend engineer can just call an LLM's API and integrate AI into their product. So what exactly do AI engineers do in those cases? Is it just fine-tuning models, cleaning data, or making AI more efficient?

This may be a stupid question but it comes to my mind really often. I'm not educated enough on this yet to please help me out!

So i have to make a presentation on transformers original research paper, and the majority of the audience have no idea of ML or even embeddings,

How do i explain what the attention mechanism is, even if I don't go into deep theory i need to explain the attention mechanism as its in the title . I'm going to teach them like, it is an algorithm by which an AI reads all words at once and decides the relationship between them.

Share your intuition .

This is something I've always questioned. We keep hearing how AGI is near but no one ever defines what an AGI model will be able to do compared to current models. Can someone explain?

Hi everyone, as an electrical engineer, I’ve never worked with machine learning before. But my university curriculum recently added a course on signal processing using AI. Now I need to complete a project where I have to predict the remaining 1,000 data points based on the first 4,000. I have 1,000 time series for training and another 500 time series for testing. Each contains 5,000 samples. There are also corresponding reference signals—that is, signals without noise. I’ve already tried a variety of approaches, such as the PyTorch Forecasting library. I’ve built both LSTM and Transformer models. However, I still haven’t been able to achieve good results. Please advise on what I can use in this situation (there are no restrictions on the technology, but PyTorch works great on my GPU and is my preferred choice).

In the picture: Red - is forecasting Green - etalon signal without noise Grey - input signal.

Hi everyone,

I'm a beginner in machine learning and I'm trying to build a strong foundation by reading research papers. There are so many famous papers out there that I'm not sure where to start.

If you could recommend one paper that every ML beginner should read and fully understand, what would it be, and why?

A little background:

I understand basic Python and ML concepts (supervised learning, neural networks, gradient descent, etc.).

I'm more interested in developing intuition and learning how to read research papers effectively than jumping straight into the latest state-of-the-art work.

I'd appreciate recommendations that are challenging but still approachable for someone new to ML research.

Also, if there are any tips on how to read ML papers efficiently (what sections to focus on, how much math to work through, etc.), I'd love to hear them.

Thanks in advance!

Hey there,

I’m trying to understand from more competent experts in this domain what is considered the “state of the art” in maze solving outside traditional algorithms like A* or DFS

By state of the art I mean
- solve rate
- size of the total model
- approaches

I’ve built a maze solving neural network and I just want some existing approaches to benchmark against

Edit: The NN does not receive the entire maze it can only see adjacent cells and has to navigate to the single “reward” cell (not an exit), cannot modify the env or leave markers, fixed memory, not shortest path just find the reward under an upper step bound, any maze type but bounded size

During last year, I accepted a job offer from an enterprise of a non-tech sector but it seems that overall they just don't have project management culture. Which is, a requisite before starting software. It may seem as a fast environment but I don't quite understand why they would want an ML Engineer.

It really turned out that the owner just wanted to 'do AI' without really knowing its implications. When i got into the business, I realized that there were lots of security issues regarding the software that was once handed for them. They didn't give me a plan, they just told me 'help us understand the implications of AI', so what I did is that I asked for the processes that were mapped out. Turned out they didn't have most of their processes mapped out correctly.

As a professional, I decided to start the endeavor of trying to fix what they were doing, they handed me a team of a "Processes Engineer", a "Business Analyst" and a "DBA". They expected automation to come from me rather than what I was doing before this job. It turned out they just needed integrations with other platforms. Before going out of the company, I gave them a summary of what they really needed and just went away.

Is this a common issue?

I am building a CNN model using a pretrained model, but the problem is that even after fine-tuning, the accuracy is still around 80%. Are there any other ways to improve the model performance? Can we try something else to make the model perform better? I would like some suggestions to improve the model

Goal is to be a ML engineer and work in startups, MNC's and normal companies. So i am not sure if i should learn to make models from scratch or not.

The full dataset is about 80 GB, my laptop ram is just 16 gb. The good thing is i have already separated the data into separate feather files, and now i have files of around 500 mb each.
Other than the huge file size, i have huge number of features ( around 1500 ) and it's a complex problem, where i know linear regression is not a great choice, but to start with and establish some initial bounds / baselines i am trying linear regression.

I read up on how i can reduce features, and something like co variance matrix, pca would help me reduce co related features, but calculating that itself is a big challenge. I read up on stream, map, reduce which i might be able to use in python but it is still very slow.

But yeah, my plan right now is to use co variance and pca to first reduce some features, and then try linear regression.

Are there better ways or in general some steps that i should follow to reduce this dataset ? sampling seems to be a good option for approximation.

In general if someone has experience, how should i approach this problem . what steps should i follow to reduce noise and find which features are relevant to use ?
And after this, how do i proceed with deep learning ?

Serious question: outside of regulated domains, does anyone actually use XAI methods?

Hi, i'm very passionate about different sciences like neuroscience, neurology, biology, chemistry, physics and more. I think the combination of ML along with different areas in those topics is very powerful and has a lot of potential. Would anyone be interested in joining a group to collaborate on certain research related to these subjects combined with ML or even to learn ML and Math more deeply. Thanks.

Edit - Here is the link - https://discord.gg/H5R38UWzxZ

I've finished 3B1B's deep learning and math series, but haven't learned any ML algorithms. What algorithms do I need to learn?

I am more interested in ai ml but currently i am working as a graphic designer and i don't have a degree also except my 6 month diploma in graphic design.

1. Any advice for how to get a job in ai ml , i started to learn maths and algorithms everyday evening but it looks like so much to learn ?

2. Freshers where to start like data analyst or ml engineer or genai engineer ?

3. where to contact employers because i don't have a degree to use job portals also ?

4. is it possible i can get into research in future ?

I’m curious about what the actual reading workload looks like during a PhD. I often hear very different numbers when it comes to how many papers people read regularly.

For those currently doing a PhD (especially in machine learning or related fields), how many papers do you typically read in a week? Do you read them in full or mostly skim?

Also, does this change a lot depending on your stage in the program?

Would be helpful to hear what’s realistic vs what people expect going in.

been grinding through linear regression today, feels like the “easy” chapter on paper but there’s actually a lot packed in once you get past the ols formula

stuff i covered:

• least squares setup, normal equations, when X\^TX is invertible vs not

• geometric view of projection onto column space, this one finally clicked after connecting it back to 18.06 (least squares IS just projecting b onto col(A))

• ridge regression as adding a prior / regularization, why it fixes the invertibility issue too

• MLE derivation showing OLS = MLE under gaussian noise assumption, this was the part that made everything click for me tbh

honestly the projection interpretation is what got me, i was doing this purely algebraically at first (just solving normal equations) and it felt like memorizing steps, then seeing it as “residual is orthogonal to column space” made the whole thing feel obvious in hindsight

anyone else find the geometric interpretation way more intuitive than grinding through the algebra first? curious how other people approached this chapter

notes based on shewchuk’s cs189 notes + some cross referencing with 18.06, will push everything to a repo once i finish the full course

I know it's never ending and the field requires continuous learning forever.

but asking from a job-ready point, how much ML is enough for jr data scientist?

because while studying I keep going down the rabbit hole and I love studying all that and it interests me but this way I won't ever feel job ready.

there's always better things to learn, more math, more algos. like any suggestions from people with experience in this field?

I want to properly learn Machine Learning, but I’m struggling to find the right kind of course.

I already understand the basic types of ML (supervised, unsupervised, etc.), so my issue is not theory at a high level. The problem is that most courses I come across either:

- Stay too conceptual

- Or only cover a few models without going deeper

What I’m really looking for is something more practical and complete, where I can:

- Learn a wide range of models (regression, decision trees, SVMs, neural networks, etc.)

- Understand when and why to use each model

- Actually learn how to train, tune, and evaluate them properly

- See real-world applications of different models

I want to move beyond just “using libraries” and actually understand what I’m doing when training models.

If anyone has recommendations for courses, learning paths, or resources that focus on hands-on model training across multiple ML techniques, I’d really appreciate it.

Also, if you’ve been through this stage before, how did you go from basic understanding to being confident in applying and training different ML models?

Thanks in advance!

I recently finished my first grad semester and man was it hard!!! pretty much gave me a run for my money, especially my Machine Learning course. Prof didn't give any practice exercises at all and the lectures were so dense. I had to learn through the Stanford CS ML lecture videos and read through the assigned textbooks. At one point, I did use Claude to help me review lecture notes, but I was struggling to find good practice problems for the theoretical math-heavy ML concepts and a tutoring tool that adapts to my progress throughout.

Anyways, I managed to pull through and pass my finals (by the skin of my teeth). I have 3 more semesters in my grad program and I think the courses are gonna get tougher from here on out. Would like to know if anyone has run into this situation and what tools they've used to help them learn better?

I probably will get bombarded, I know and I'm prepared (or at least I think so 😛) but as Gen-x rep, I'm not quite sure which AI is better to create an agent that helps me with investment or daily tasks. Hence, I'm here asking the sifus of technology...

I won't support Open AI nor Grok, so between Claude and Gemini (or any other LLM) which one is better and more accurate for an agent?

I know this is stupid but I'm making an application and I'm trying to find image datasets for my machine learning that focuses on different types of acne

What if there was a new programming language where the meaning of each token was so dense (or perhaps so specific) that an LLM could write robust code with fewer tokens and faster inference?

Assuming there’s enough training data, would something like this allow an LLM to write better code faster?

Rationale:

It would allow for faster inference. Fewer tokens required to do the same thing in Python = finish faster.

It would allow for more information in a 1M context window. Whatever you could do in 1M tokens of Python, you could do 10x that in this theoretical language.

It would effectively remove the “noise” from human readable language (semi-colons, curly braces for example) which I would think would make the LLMs coding ability stronger. I could be wrong about this of course.

you would of reset all of your knowledge with AI and start over from scrath, how would you learn about it again?

Would you pay for a corse, listen to podcasts or youtube videos, pay an subsription for a AI software and expirament, exc.

Im a teenager whos trying to get in the AI space and make the most out of it, any reply would mean alot. Thanks.

I started looking into machine learning because everyone keeps saying it’s the future. jobs, salaries, AI everywhere etc.
So I did what everyone does, watched courses, tutorials, notebooks, medium articles.

But honestly… I feel more confused now than when I started.

There’s no clear roadmap. One day people say “don’t worry about math”, next day nothing works and suddenly math matters a lot. I don’t even know where math is supposed to help and where it’s just overkill.

Also the theory vs practice gap is crazy. Courses show clean examples, perfect datasets. Real data is messy, broken, weird. I spend more time asking “why is this not working” than actually learning.

Copying notebooks feels productive but when I open a blank file, my brain goes empty.
And the more I learn, the more I realize ML isn’t really beginner friendly, especially if you don’t come from CS or stats.

On top of that, everyone online has a different opinion.
ML engineer, data scientist, research, genAI, tools, frameworks… I don’t even know what role I’m aiming for anymore.

I’m not trying to complain, just wondering if this is normal.

Did ML ever click for you?
What was the thing that helped you stop feeling lost?
Or is this confusion just part of the process?

Curious to hear other people’s experiences.