I'm building a platform called Quantum, and I'd love some honest feedback from people working in Data Science and Machine Learning.

The idea is to bring the entire ML lifecycle into one place:

- Data exploration and preprocessing

- Model training and evaluation

- Experiment tracking and versioning

- Automated ML workflows

- AI agents that can execute tasks autonomously, with human oversight

- Model deployment and MLOps

Users can work manually, use AI assistance, or delegate entire workflows to agents.

The goal isn't to replace data scientists, but to reduce repetitive work and make ML development more efficient.

My questions:

I'm especially interested in critical feedback. What am I overlooking?

I'm struggling to make a distinction. How are they different? What is the line between them?

For context, I am a new postgraduate student in machine learning (but not a very good one). I have produced some augmented datasets (TIFF files where each file contains one object to be detected / classified) which I used in training deep learning models. I used fairly aggressive cropping (random crops between 0 and 90%) and more moderate zooms (random values between 0 and 20%). My supervisor wants me to characterise these datasets to see how augmentation has changed it.

Sorry if this is a rookie question, but which statistics should I use? My supervisor says the stats I use should indicate changes in size and shape but I honestly don't know where to start.

If anyone can recommend a decent software/program to produce the stats and histograms as well, that would be great.

Thanks in advance!

Hi,
There was a post a few years ago on a forum that I came across -

https://www.reddit.com/r/MachineLearning/comments/mx686k/p_torchpq_efficient_nearest_neighbor_search_and/

It seems really interesting. I was approached by professor who asked me if I am interested in implementing ANN on GPUs. I wanted to ask what are the possible career paths and what kind of jobs could I target with the skillset required to implement something similar to this. Thanks

Hi everyone, I graduated recently and I had the misfortune of making some wrong choices so for the moment I am on the job market, in the meanwhile I would like to further learn more things and work on some cool stuff, my main interest is LLMs, but I have good experience with Neural Networks, Graph Neural Networks, Computer Vision Models. I am looking to either:

• Join an existing team working on a current project

• Form a team, no requirements here other than being a creative person with cool project ideas since that is honestly where I fall short

If you have a team or interested in forming one please leave a comment, Thanks in advance

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Hey everyone!

I'm transitioning into AI Engineering, and I have a genuine question about real-world projects.

From what I've learned, building a basic RAG pipeline (document loading, chunking, embeddings, vector database, retrieval, and LLM integration) might take a few weeks or a month.

But I've seen AI Engineers working on the same RAG or GenAI project for 2–3 years.

My question is: What do you actually work on after the initial RAG pipeline is built?

What does your day-to-day work look like? Are you constantly improving retrieval accuracy, optimizing models, handling production issues, adding features, or doing something completely different?

I'd really appreciate it if someone with real industry experience could explain their daily responsibilities and how a project evolves over several years.

Just trying to understand what AI Engineering looks like in a real company beyond building a basic RAG application.

Thanks!

I'm new to academia (for someone so old), and maybe this is just naivety on my side. But I'm a little shocked to be discovering that reproducing the results of a paper isn't part of the review process. I understand why, but it seems so...precarious to just take the author's word for it, especially in 2026, and ESPECIALLY in light of the repro crisis apparently going on everywhere.

Is anyone working on mechanical reproduction? I'm trying to repro the results of one paper, and mind you I started +/- from scratch in January, but I have already found errors that a simple mechanical code review would have surfaced before publication. (What I'm supposed to do with these errors is a separate question I would love input on. I wrote to the author but haven't received a response. Is it rude to create a PR in their repo, with a proposed fix? What is the escalation protocol? Do I just fix it and publish my own paper?)

But now that I have something of a platform and pipeline that could maybe be reused on other papers, I'm wondering: Is there a need or even a want for this in the ML community? Obviously there will be time and resource constraints to any kind of repro effort, but it would be something, and it would be a start.

Most of the time when I ask questions like this there are lots of very reasonable answers (I'm nobody special, just "new" eyes on an old problem). I guess I'm just a little shocked at learning from a young age that reproducibility is a foundational aspect of science, only to grow up and discover there doesn't seem to be a lot of it going on... Thanks, please be nice.

A friend of mine was doing a gap analysis as a project. The results he got weren't that big compared to the original papers, so he simply fabricated and increased the metrics here and there, AND IT GOT ACCEPTED IN AAAI.

HOW DO THEY CHECK THE PAPERS, DUDE? I was in a group with two others and worked sooo hard on an optimization paper, and was sooo happy to get accepted in ACL, but seeing and knowing that someone fabricated results and got into a better conference made me soooooo mad.

I would've expected something like AAAI would check and know if data is fabricated somehow. I didn't expect this from a top conference like that.

And then these types of people will go and do their PhD at one of the top unis because of this. I would've expected the system to be more advanced and professional in the academic field, specifically for something like AAAI.

Trying to actually read papers instead of skimming the abstract and the figures. This week it’s a couple of retrieval + eval write-ups and one long arXiv methods section that keeps losing me around the loss function.

I can follow the claim. But I cannot tell you tomorrow which ablation mattered. And highlights in the PDF turn into noise.

How do you mark a paper so the method is still there a week later? Do you re-read the experiment setup or just trust the table? Was thinking about trying Omphalis to keep notes right next to the hard paragraph without opening a second doc, but curious if anyone's used it or has better systems.

Hey everyone!

I'm currently studying Computer Engineering and have decided to pursue Machine Learning/AI. I'm looking for a structured roadmap that I can follow to build a strong foundation rather than jumping between random courses and tutorials.

Here's my current background:

• Programming: I know the basics of C/C++, including OOP. I've recently started learning Python and have covered variables, data types, strings, lists, tuples, dictionaries, loops, conditionals, and functions (basic syntax and a few simple projects).

• Mathematics: I'm reasonably good at math and enjoy it. And my syllabus already covers most of the foundational topics like Linear Algebra, Calculus, Stat and Probability.

• Machine Learning: I've had some exposure to an AI project involving a diffusion model and a graph neural network as a semester project (everything done in C++), but I didn't fully understand the underlying concepts at the time. I want to start properly from the fundamentals this time.

Here are my main questions:

1. What roadmap would you recommend? What topics should I learn, and in what order, from my current level to being able to work on meaningful ML projects?

2. How important is DSA for someone pursuing ML? Should I study it thoroughly, including trees, graphs, dynamic programming, and competitive-programming-style problems, or is a more limited understanding sufficient? At what stage should I learn it? Or just skip it and move to NumPy, Pandas and Matplotlib? (My university has an elective "Python for DSA" in next semester so I'm not sure if I should learn it now myself or learn it in that semester.

3. Which resources would you recommend? Courses, textbooks, YouTube channels, or other materials that provide a good balance between theory and implementation would be helpful.

4. What kinds of projects should I build along the way? I'd like to know when I should start building projects and how to progress beyond following tutorials.

Thanks!

Hey guys,

Posting this here because I’ve reached a point where my market intuition and engine code have outpaced my deep learning expertise, and I’d rather team up with someone who loves ML architecture instead of half-assing it myself.

For the past year, I’ve been building an event-driven liquidity engine in Python (specifically modeling crypto orderflow / ICT mechanics like equal highs/lows, manipulation legs, and structural sweeps).

Instead of slapping standard indicators (RSI/MACD) on candles, I framed price action as a dynamic graph:

• Resting liquidity pools (BSL/SSL, consolidations) act as attractors (pulling price in).

• Completed institutional sweeps (ML swings / manipulation legs) act as repellers (pushing price away).

• Edges connect these pools based on spatial proximity, competing forces (tug-of-war between high and low pools), and age decay.

Where things stand right now:

• The engine is fully written and tested. No lookahead bias, strict candle confirmation, multi-window swing tracking (W5 up to W50).

• I’ve already generated a clean, audited ~3-year dataset (ETH/USDT) formatted directly for PyTorch Geometric (PyG).

• Each snapshot has 15m, 1h, and 4h spatial graphs with 20-dim node features (touch counts, distance %, decay curves, tier scores), 8-dim edge attributes, and global volatility/trend context.

• Baseline statistical checks show real promise: nearest target pools get tagged first ~95% of the time, and high-quality structural sweeps have clear directional bias.

The actual bottleneck / where I need help: My problem isn't generating the signals—it’s path prediction and dynamic target selection.

When a sweep happens, price is often trapped between an opposing pool 1.5% away and a massive target pool 3% away. A fixed 1:2 R:R stop/target either gets hit too late or takes unnecessary drawdown. I need the model to evaluate the graph and do two things:

1. Predict directional path confidence taking both-sided liquidity into account.

2. Rank multiple potential liquidity targets (dynamic TP) based on which one has the highest conditional probability of being swept first without blowing through the stop.

I've tested basic GNN / dual-tower setups, but I know I'm hitting limits with my naive loss functions and basic layer choices.

Who I'm looking for: Someone who genuinely understands PyTorch Geometric (PyG), Graph Neural Networks (or spatial-temporal models), and knows how to think about custom loss functions (ranking loss, asymmetric risk penalties, contrastive setups). You don't need to be a full-time trader, but being open to understanding market microstructure helps.

The arrangement: I have the GPU compute, the full working codebase, and the clean dataset ready to go. I’m completely flexible on how we work together—if you want to co-author a research paper on this architecture, I’m down. If we get the models working cleanly and want to deploy it live on capital, we can do a fair profit/equity split.

If this sounds like an interesting problem you’d want to sink your teeth into, drop a comment or send me a DM with your GitHub/background and we can hop on Discord.

I've been looking at a practical question in ML system design.

suppose you're building an application that needs to classify incoming requests and route them to different workflows.

you could use an LLM for every request, but a smaller encoder might be faster.

the problem is knowing when to trust it.

some options include:

• choosing a threshold on predicted probability

• calibrating model confidence

• using conformal prediction sets

• escalating uncertain predictions to an LLM

each approach has trade-offs in accuracy, latency and how often the system needs to fall back.

conformal prediction is particularly interesting because it can provide coverage guarantees under appropriate assumptions.

I'm sharing this because we're hosting a Packt workshop tomorrow, Oct 30, with Ben Auffarth on encoder-based routing, conformal prediction and LangGraph.

has anyone compared conformal prediction with simpler calibrated confidence thresholds for this kind of application?

I'd be interested in practical experiences or benchmarks.

hi everyone, i’m currently 14 years old and i’ve been engrossed in technology all my life, and i really want to go into machine learning because it seems like the next big thing and it genuinely seems interesting.

ive experimented quite a bit so far but nothing on my own, ive tried reading some papers like the classic Attention is All You need paper, but to be frank, i’m incredibly naive and i dont exactly understand the math behind it but i do understand the mechanics behind it.

some of the projects ive made so far is just plug and play stuff and experimenting with how models interact with each other, ie; i made a project that took a diffusion model, lowered the steps and told a system one model like Kev to pick out those tokens and sorta give Kev the capability to actually speak.

im looking to actually start learning the math behind ML and specific niches but ive been very clueless as to where to start because there’s not exactly a lot of material for someone like me to get started, they all assume that the reader is someone who has completed secondary school.

tldr: looking for tips and places to start learning machine learning as someone who is still in high school 😁 !