I am a third-year economics student, and a few weeks ago I started wondering about ML (perhaps a bit late? I’m not sure). I wouldn't call myself a die-hard enthusiast of this vast field, but I’ve already signed up for an elective course called "Application of Machine Learning and Optimization", though I don't want to wait until then to get started. I know for a fact that this will be important in my future professional life or, at least, it’s certainly not knowledge that goes to waste.

My question is both simple and complex, I’m not sure which:

Given that many experts today say AI spells trouble for programmers, how - and from what perspective - should I go about learning to use programming and ML tools?

Just as the paradigm has shifted, the path to mastering it must have changed as well.

I'm working on an ML project where I'm trying to keep model versions, artifacts, deployments, predictions and evaluation metrics tied together instead of managing them separately.

I'm curious how people here usually approach this.

For example:

• How do you track which model artifact belongs to which version?
• How do you handle deploying different model versions?
• Do you keep inference metrics alongside the model/version metadata?
• At what point do you reach for an existing ML platform instead of building this yourself?

I'm especially interested in hearing how people handle this in smaller projects where something like a full ML platform might be overkill.

Been building RAG and LLM-powered features and realized my "evaluation" process was basically reading a handful of outputs and deciding it looked fine. No versioning, no regression testing, no real way to know if a change actually helped or if I just got lucky on the examples I happened to check.

Curious how others here actually do this in a real setting. Are people running statistical significance tests on model or prompt comparisons (bootstrap confidence intervals, paired testing), or is it mostly still qualitative review? And for retrieval specifically, is anyone actually tracking recall@k or MRR, or just eyeballing whether the right chunk shows up?

Found a hands-on masterclass on Sep 12 that goes deep into building this kind of eval harness properly (deterministic checks plus LLM-as-judge, statistically rigorous model comparisons, evaluated RAG, agent guardrails, full observability), so I'm curious if the methodology it teaches lines up with how experienced people here are actually doing this, or if there's a simpler approach I'm missing.

Link for more details

Hey everyone,

I have an upcoming ML Design interview at Tesco and I’d really appreciate some guidance on how to prepare for it.

If anyone has recently gone through the ML Design round at Tesco, could you please share what kind of questions were asked or what areas I should focus on? I’d especially like to know whether the questions are around recommendation systems, forecasting, fraud detection, NLP, or general ML system design.

Any guidance or interview experience would be really helpful. Thanks in advance! 🙏

What is the best part to become a ML enginner? And what are the best platform to learn Free?

Hi everyone,

I’m Spanish, but I currently speak both French and English professionally. My goal is to create a high-quality voice clone with ElevenLabs that sounds like me speaking French and English, including my natural Spanish accent — I’m not looking for it to sound like a native French or English speaker.

What would be the best way to record the training audio?

- Should I record everything in Spanish, since that’s my native language?

- Should I record mainly in French and English, so ElevenLabs learns how I actually sound in those languages?

- Should I create a mix of Spanish + French + English?

- Is there an optimal proportion or amount of audio for each language?

- And should I deliberately use my normal accent/pronunciation rather than trying to speak perfectly?

I’m aiming for a professional-quality clone for business/content creation, so I’d really appreciate advice from anyone who has experimented with multilingual voice cloning in ElevenLabs.

Thanks!

Good evening,

I have a bit of a challenging situation. I'm an amateur historian, I was able to receive 370.000 photos of WW2 Microfilm reels. These images are of various quality and contain mostly typed content.

I'm looking for a way to have them indexed so that I'm not required to manually go trough each and every file. I guess that the images have to be put trough OCR first. But how to do that and continue from there is hard to gather.

Is anyone around here able to help me start this project?

Regards,

Remi

We recently ran an empirical benchmark across five model scales (Qwen-1.5B, Mistral-7B, LLaMA-8B, Qwen-27B, and a 120B frontier model) evaluating self-consistency and uncertainty estimation on reasoning and factual QA.

Up to 27B parameters, sampling multiple outputs at temperature 0.7 gives great diagnostic signal. Measuring consensus entropy across paths achieves an AUROC of 0.889 in separating correct answers from hallucinations.

At the 120B scale, however, we ran into an interesting failure mode: even at T=0.7, the model generated the exact same incorrect answer across all 10 independent seeds. Because every path agreed 100% on the hallucination, self-consistency saw zero entropy, inverting AUROC to 0.091.

Our current hypothesis is that intense RLHF/DPO alignment hyper-peaks the output logits to penalize epistemic hedging, effectively collapsing the sampling diversity on ungrounded facts.

Preprint and code with the benchmark tables: https://zenodo.org/records/22233648 and https://github.com/Adarshent/Spnda

For those working on calibration or post-training: is this recognized as a standard failure mode of DPO/PPO, and are there decoding-time interventions that can decorrelate samples without degrading coherence?

Hello, I'm currently 17 years old and I'm thinking ahead for a career in the tech industry, specifically biotech. I'd be gladly hearing from any active workers in the industry. Is your day-to-day working environment exciting? Is biology/medicine knowledge highly valued? Are you working a lot with biology or focus more on coding/tech stacks? I'm not a tech enthusiast by any means, would i feel at home with this job? And most importantly, are remote jobs in the industry even real?

Hello! Quick context about myself, I am a senior in my undergraduate program in Chemical Engineering. I have for the major part of my undergrad worked on more conventional problems in Chemical Engineering, but ever since the start of my undergrad thesis, I've been working on using fine tuned ML models, for quantum level calculations for finding out materials to capture Carbon Dioxide. Practically High throughput screening of materials using GNNs and Transformers. Prior to this, I also worked on developing a surrogate model, that given composition and atomic parameters of a material, could give out its catalytic properties in a specific context.

Off late, I have been considering shifting out from traditional Chemical Engineering, to more AI for Science, essentially around Quantum Physics/Chemistry, and given my background, I feel it might be a bit problematic to do so.

Most pre-doctoral programs I've come across don't let fellows work on the set of problems I've worked on, and a PhD in Chemical Engineering might actually sift me further away from this.

Given that, I wanted to understand what options I have for getting into this area of research, and how can I improve my profile.

Further context: My other works (which have been published) involve more base chemical engineering problems across Energy and Reactor Modelling, which as you can guess is way too far off from this. Also, as a project for a university course, I worked on a token reduction method for allowing Transformers to have a higher throughput, which worked on scoring groups of tokens dynamically varying based on importance, and reducing fluff, and generating a summary vector which goes on to a minute 40M param model. Nothing crazy, but worked decently on the WikiText set, and had a reasonable perplexity post training, but yeah, couldn't mess around more with it, due to having limited compute.

I have been ideating on a few things for more concrete stuff in the quantum application space itself, but not so sure of it at the moment.

Open to any and all suggestions, for how I could move ahead, and what options I should consider.
Thanks for the help!

Course is new enough that every review I find is either an affiliate roundup or an AI summary of the marketing page. Class Central has ratings and no detail, and the Udacity and Coursera pages both read like brochures. If somebody has the syllabus in front of them I would take that over a star rating.

I have a set of roughly 150 documents of about 1500 words each that describe technical concepts (higher level findings from a collaborative research project). I want to find out if there are thematic cross-links between these. Like upper-level or lower-level concept; parallel or alternative concepts.  Think about a very very small wikipedia with lost links that need to be restored (but on the concept level; not at the word level).

How can do this programmatically, maybe using LLMs?

That may be a trivial task for many of you here but I feel a bit stuck at the moment. I thought about asking my human colleagues for classification support, but even my very small document set yields >20.000 potential cross-links.

Hi everyone, I have a question about choosing the appropriate background dataset when calculating SHAP values. I am using the kernelshap package in R, where we provide an X dataset containing the observations we want to explain and a bg_X dataset defining the background.

I have a binary classification model for disease vs non-disease, trained on a derivation dataset and evaluated on an independent validation dataset. My current understanding is that, if I want to explain predictions in the validation cohort, it makes sense to use the validation set as X and the derivation set as bg_X. In that case, the SHAP values for validation patients would describe how each feature moves their prediction relative to a baseline defined by the derivation population. Is this interpretation correct, and is this generally the recommended way to use the background when explaining an independent validation cohort?

My main question is about a more specific analysis. Suppose I want to investigate heterogeneity within patients who truly have the disease. More specifically, I want to see whether different disease patients receive high disease predictions through different combinations of features, and potentially cluster these patients based on their SHAP profiles.

In this case, I assume I should use only the true disease patients from the validation cohort as X, since those are the patients whose predictions I want to explain. However, I am unsure about the most appropriate choice for bg_X. Should I keep the full derivation cohort as the background, use only disease patients from the derivation cohort, or use the disease patients from the validation cohort themselves as the background?

If my main objective is to determine whether true disease patients have different model-attribution profiles, potentially reflecting different features through which the model identifies them as disease, which background would be the most statistically appropriate? Thank you!

Hi group, I’m trying to train my AI models, but looking fot good reputable data (I am no data scientist so apologies if I sound redundant ) I work in clinical trials but in my spare time love learning about Ai, I want good data sets/ but reputable ones to train my model

Its a niche industry/question but just want to ask here for advice pls

I watched a YouTube video that said if you want to get your first job as a machine learning engineer, you should do volunteer work, especially in GitHub repositories, but I'm not entirely sure this is true, and if it is, where can I find these repositories that require this kind of work? I need it because I want to gain better knowledge and understand real world problems.

I am trying to build a reliable workflow for following industrial visual anomaly detection research, not just a list of recent papers.

The difficult part is knowing when reported results are genuinely comparable. A leaderboard number can change because of the train/test split, supervision level, pretraining, image resolution, category averaging, or the exact AUROC/AUPRO calculation. For edge use I also need hardware, latency, memory use, and reproducible code.

I currently check arXiv, OpenReview, conference proceedings, Hugging Face Papers, GitHub repositories, and Papers with Code mirrors or datasets, but none of them seems to preserve the complete paper-to-code-to-benchmark workflow reliably. Manual spreadsheets work, but become stale quickly.

For people working with MVTec AD, VisA, BTAD, or similar datasets: what sources and process do you actually trust? Do you maintain your own comparison table, follow particular labs or repositories, or use a benchmark suite that normalizes evaluation protocols? I would also appreciate examples of fields you record to avoid comparing incompatible results.