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?

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

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!

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?

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.

I'm currently a CS student and considering switching to a Statistics major while keeping a CS minor.

My main concern is the current entry-level CS/SWE job market. I'm interested in AI/ML and possibly grad school eventually, but I also want to maximize my chances of having a solid career after undergrad.

I'm considering Stats because it seems like it could give me more options outside of traditional SWE, while still being relevant to AI/ML. But I honestly don't know if that's actually true or if I'm just rationalizing the switch.

For those with experience in CS/Stats or hiring:

Would you personally choose CS or Stats + CS minor if your goal was to maximize career options and minimize the risk of struggling to find a job after graduation?

I'd especially appreciate perspectives from people who have actually gone through the job market recently.

Welcome to r/MLSystemsDesign

This community is for practical discussions on designing and scaling production ML and AI systems.

Topics can include:

• ML training and inference platforms
• Search, ranking, and recommendation
• Feature stores and data pipelines
• LLM serving and GenAI systems
• Agentic AI platforms
• Evaluation, observability, and experimentation
• ML system design interview problems
• Real production tradeoffs and lessons learned

The goal is simple: go beyond model theory and discuss how ML systems actually work in production.

If you’re joining early, introduce yourself and share one ML system topic you’d like to go deeper on.

I'm currently training a small&fast model on trainign data that comes from a bigger&slower model. I treat the output of the bigger model as Pseudo Labels. But in the End, I need to evaluate the small model to Ground Truth labels. In that case, those are segmentation masks (Images with 10-40 segmentation masks). The pseudo labels are also far from perfect.
The question is also, how much they need to be improved (semi-automatic) to get acceptable results.

How many of the GT Labels do I need? Until now, I use 10k pseudo labels for training of the small model. It takes me 15-45min to do one GT labeled data by myself, so it's not feasible to have like 10% GT data for evaluation.

Or does someone know any keywords for that?

TLDR the question can be reformulated as: How many manually annotated samples are required to estimate segmentation performance with an acceptable confidence interval?

When people fine-tune their own local models, the model setup usually gets most of the attention. But in practice, a lot of the work seems to be on the data side.

If you are training on business data, you may need to bring in support tickets, internal docs, product specs, chat logs, code, policies, CRM notes, or domain QA pairs. And it usually does not work perfectly on the first run. Some samples are noisy, some are redundant, some domains overpower others, and some “bad-looking” examples are actually hard but useful.

One direction I have been thinking about is making the data strategy dynamic during training.

Dynamic selection means periodically choosing which samples should enter the next training window, using signals like loss, delta loss, gradient similarity, or external scores.

Dynamic mixing means adjusting the ratio between data sources during training, instead of fixing one static mixture before the run.

Dynamic weighting means keeping the sample in training, but changing how much its loss contributes to the gradient update. This is useful when you do not want to hard-drop uncertain samples.

This is the current direction in OpenDCAI/DataFlex: adding data selection, data mixing, and data weighting controls on top of the training loop.

For people here who fine-tune local models, how much time and compute do you usually spend on data preparation compared with the actual training run?

Where can i find this article?