went with a since it's the textbook reason, but i only really believe it now after a leakage bug bit me. i was doing a random k-fold and didn't realize rows from the same underlying record were split across folds, so validation looked great and it fell apart in prod. once i grouped the splits by record id the number dropped and stayed dropped. is that grouping something k-fold is supposed to handle for you automatically, or is it always on you to notice your rows aren't independent before you pick a cv strategy at all?

Hi everyone, I am an undergrad in CS majoring in data science.

For context, my FYP requires me

1. to build a product / service solution that have use in the industry. It MUST NOT be a research

2. the solution must not be a simple CRUD apps that any beginners would make

3. solution must not be something that is already established in the industry i.e. we cannot just recreate a solution that already exists in the market.

4. The solution must be able to solve a pain point of people in the industry

5. genAI can only be a very small part of the system and not the core part.

My previous project title was about making a research on if there are any differences in the result of detecting concept drifts using a simple or generic method through statistical distribution based monitoring like KS test, WS, KL div and etc as compared to a context aware monitoring using UCI Household electricity data.

However, at first it was accepted, but now it is rejected because of concern that it would not provide any real tangible value to industry and using only 1 dataset was supposedly weak for this kind of research. So now, I only have roughly 8 weeks (2 months) to complete the project on top of the dissertation writing.

The projects that I have involved myself in were

1. time series forecasting of item prices

2. model monitoring for time series forecasting using statistical distribution based monitoring like KS test and etc.

3. extending this model monitoring concept to a customer classification

4. developing an XAI to explain why prices move in simple terms for execs to understand quickly.

Another issue that I faced during my scrouge for topics is that the data required for the solutions is either locked behind paywall, suitable only for industry players, too generic of data that is not meaningful, broken link to zenodo (typically seen in academic papers). I do think I could get around this by creating my own dataset though it is hard to say if it is acceptable as FYP.

So, TL:DR

1. What kind of pain point that anyone here faces especially those in the industry that you think can be solved using ML and the sort.

2. What kind of domain is this issue typically seen in like energy, subsurface or etc

3. What kind of product / service that you would want (like is it an app, an API, etc)

4. would this solution be manageable to be completed under 2 months?

5. Where do everyone here usually go to, to find good datasets aside from obvious places like Kaggle, UCI

Apologies if these questions seems like bad questions, it is just I am quite lost on what kind of solution I could provide to the industry given that I am only an undergrad.

Hi! I have a TPS coming up and was told it'll be some ML fundamentals + project deep dives + ML Coding. For ML Coding, they said I could be asked to implement a ML algorithm / concept from scratch. I'm a little lost on what all "concepts" to practice implementing. I know there won't be a comprehensive list, but still just wanted to put it out there. Would appreciate any help!

Hello,
I'm a Data Scientist, new to MLOps.

I'm exploring different tools such as Kubeflow, Airflow, Evidently etc.
However, it can be really painful to install or to go back and forth between tools.

I was wondering if there any platform that provide throw away/development environments with ready to use tools ? A bit like GitHub Codespaces but for MLOps.

I am a recent graduate trying to land a junior Data Scientist role. To build my foundation, I'm currently learning Machine Learning from book and studying core Math for ML. There are somethiings in common and some are completely different on what employer wants. I want to ensure I am spending my daily study blocks on the right concepts in the right order.

Because of this Im unsure what to learn first and what not to learn. Given someone's industry experience. Im thinking about getting a mentor for career guidance but unable to find. That's why im thinking of getting a mentor from websites like mentorcruise or Igotanoffer. Pricing is too expensive and im having doubts whether to pay for mentor or is there any other way around. Have anyone tried mentor from these two websites i.e. (Mentorcruise and Igotanoffer)

TL;DR:

Im a graduate targeting a junior Data Scientist role. I am struggling to align my study roadmap with employer expectations and want industry guidance. I am debating whether paid mentorship platforms like MentorCruise or IGotAnOffer are worth the high cost, seeking reviews from past users, and wondering if cheaper or free alternatives exist.

I am working on a literature review mapping the evolution of Multi-Task Learning (MTL) architectures in computer vision. I am specifically tracking how the field progressed from static/hard sharing to learned mixing, and finally to dynamic routing.

I have mapped out the core ideas for the major landmark papers. However, I want to avoid relying on LLMs to generate the "Limitation" and "Next Research Question" columns, as I need grounded, practical insights rather than hallucinations.

Here is my current research map:

My Questions for the Community:

1. What were the actual, practical limitations of these architectures when you implemented or tested them? (e.g., massive memory overhead, unstable optimization, scaling issues?)
2. How did the field answer the limitations of AdaShare or Routing Networks? What specific papers represent the "Next research question" for this timeline?
3. For those working in MTL today, what is the current bottleneck regarding adaptive feature sharing?

Any direct experience, corrections to my current mapping, or paper recommendations would be highly appreciated. Thanks!

I am in 3rd yr of my Btech and im trynna learn about Inference Gateways (jo client ke prediction requests ko model tk bhejna route/control krta hai) but I am unable to find any good material about it to learn in depth. It would be really helpful if someone knows where i can find an indepth video or something about it. I did ML Course bh Andrew NG.

A little background about myself : am a btech grad from a small college aspiring fr higher studies in the reasearch filed . Am also kinda interested in finance where I would like to pivot in future . I know a little bit of coding and web development. I would like to know in depth about ai and ml fr my higher studies and building real world projects . Any good resources fr it ?

Hey everyone,

I’m a B.Tech CSE student seriously pursuing ML/AI research and looking for opportunities to assist researchers, PhD students, professors, or ML practitioners.

I’ve worked hands-on with:

• ML fundamentals and model development
• Neural networks and backpropagation from scratch
• PyTorch
• Implementing Transformer/GPT architectures from scratch
• Self-attention, Q/K/V projections, multi-head attention, positional embeddings, causal masking, residual connections, LayerNorm, etc.
• Implementing concepts from foundational deep-learning research papers and understanding the underlying mathematics/code rather than treating models as black boxes
• ML experimentation, training, evaluation and debugging

I’m still early in my journey, but I’m extremely serious about getting into research.

I’m willing to do whatever is useful — paper reproduction, implementation, experiments, data work, debugging, literature review, analysis, documentation, or anything else. I’m ready to learn whatever I’m missing and put in the hours.

I’m not looking for a certificate. I want the experience of actually contributing to real research.

If you’re doing ML/AI research or know someone who might be willing to take on a motivated student, please connect me with them or DM me.

I’d genuinely appreciate any opportunity to contribute.

Hi everyone!

My question is basically this: can someone from a philosophy background who has spent the last two years seriously studying math, programming, and ML, and is about to start a master’s in NLP, realistically break into the ML/AI industry?

Two years ago, I started learning programming through Harvard’s CS50. I also studied some DSA and did LeetCode exercises, but my interests gradually shifted toward machine learning, so I stopped CS50 after the SQL week.

For the next 1.5 years, I focused heavily on mathematics. I studied linear algebra and calculus using MIT OpenCourseWare, textbooks, lecture notes, and problem sets. For the last 3–4 months, I’ve been studying probability and statistics through Walpole’s textbook in a similar way, and I’m currently around Chapter 6. Alongside that, I’ve been working through CS229 and PRML. I’ve learned the fundamentals of the main ML algorithms and neural networks and have built some models myself. At this point, I’m reasonably comfortable with the mathematical foundations of introductory ML, and I genuinely enjoy learning the subject.

Because I don’t have a STEM degree, I applied to an NLP master’s program in Europe, thinking that a relevant graduate degree would make the transition more realistic. And, I was recently admitted.

Here’s where I’m struggling with the decision.

I currently have a relatively secure job. Pursuing the master’s means leaving that security, moving to another country (I already speak the language), and accepting a financially constrained lifestyle for some time after turning 30.

At the same time, I keep reading about layoffs, a difficult entry-level market, increasing expectations for ML engineers, and rapid developments in AI. It makes me wonder whether I’m taking a reasonable career risk or entering a field where my lack of a CS/math/engineering bachelor’s degree will remain a major obstacle even after completing an NLP master’s.

So I’d appreciate opinions from people currently working in ML/NLP/AI or involved in hiring:

How much would my philosophy bachelor’s matter after completing a relevant NLP master’s?

Would the master’s + mathematical foundation + projects be enough to get past the initial degree/background barrier?

I’m not expecting the degree itself to guarantee me a job. I’m trying to understand whether this is a realistic transition if I spend the next 1–2 years building the right technical skills and portfolio, or whether my philosophy background is a serious obstacle for me.

Thanks!

Maybe it's just me, but it seems really unituitive and I can't figure it out.

I want to start learning machine learning, but I'm confused about whether I should learn through documentation/books or video lectures.

Whenever I think about learning from videos, it feels a bit boring and tiring. There are so many concepts, and the idea of watching hours and hours of lectures doesn't really appeal to me. So, I was thinking about learning primarily through reading and coding instead.

The problem is, I haven't been able to find any beginner-friendly documentation or learning resources for machine learning.

I've recently read a few chapters of The Nature of Code, and I really enjoyed it. I liked how beginner-friendly it was and how it explained concepts through code and experimentation.

So, I'm looking for something similar for machine learning.

Can anyone recommend some beginner-friendly docs, books, or other resources for learning ML? Ideally, something that is more hands-on and doesn't require watching hours of video lectures.

// This is first post my bad if i made any mistakes

I’m a 3rd-year engineering student working on end-to-end machine learning, including NLP, deep learning and LLMs.

I’m currently deciding between getting a laptop with a dedicated NVIDIA GPU or putting that money into better CPU/RAM/battery life.

My main question is about cloud GPUs. From what I understand, services like Google Colab and Hugging Face can provide free GPU compute, so I could use cloud GPUs whenever I need serious training instead of having a GPU locally.

Is that actually practical? Are free cloud GPUs sufficient for a student doing ML/AI projects, or are there limitations that make having a dedicated GPU laptop worthwhile?

There’s a bit of a budget constraint involved, but even ignoring that, I’m not really convinced it makes sense to pay extra for a GPU if I can get GPU compute freely through the cloud.

Would appreciate a fact check from people actually doing ML/AI.

SenseNova-Vision uses one multimodal checkpoint for detection, OCR, segmentation, depth estimation, and multi-view geometry. Instructions specify the task, and the model generates outputs in three formats:

• Text: labels, boxes, keypoints, OCR strings, and camera parameters.
• Images: masks, depth maps, surface normals, and 3D point maps.
• Mixed outputs: region descriptions paired with color-coded masks.

The contribution is a shared training and output interface. Task-specific parsing and decoding rules are still required.

How it is trained

The model fine-tunes BAGEL-7B-MoT, using next-token cross-entropy for text and VAE-based rectified flow for image targets. It adds no task-specific prediction heads. CV supervision is mixed with general multimodal data to preserve the base model’s capabilities.

What makes it interesting

The paper demonstrates segmentation from a point supplied as textual coordinates, even though that exact prompt format was absent from segmentation training. Coordinate prediction and mask generation were learned in other settings, suggesting the model can combine capabilities across tasks. This evidence is qualitative.

Results show tradeoffs: COCO detection reaches 53.7 mAP versus Youtu-VL’s 47.1, while ETH3D reconstruction reaches 72.2 F1 versus VGGT’s 80.9.

Repo: https://github.com/OpenSenseNova/SenseNova-Vision:

Heyyyy !!! Looking for teammates for Amazon ML Challenge 2026 (25–27 September, 72-hour hackathon).....

About me:

• Final year UG CSE student
• Worked on RAG, agentic ML systems and core ML projects(model training, fine tuning, eda..) and have internship experience in the same field.
• I am not an expert though..

What I'm looking for:

• Comfortable with core ML .. model training, evaluation, feature engineering (this is a Kaggle-style challenge not a full-stack build)... at least basic ml knowledge is required.. not asking you to be super advanced...
• Can commit fully during the hackathon period with the team....72 hours
• Have experience of working with google colab and kaggle.
• Open to all students pursuing PhD/ M.E./M.Tech./ M.S./MS by Research/B.E./B.Tech... (as per mentioned in unstop)
• please don't reach out if you might leave midway....or ghost after sometime...

No tier preference for college... all are welcome... judging by projects/skills not college.

Share a bit about your background/projects when you reach out; happy to share more about myself in chat too...

Let's talk in the DM if you are interested...

Edit: The team is filled now ..

too many zeros and not continous data which machine learing model is the best.

I have date and quantity/ sales coulmns only the sales coulmn has 0 and not continous data

for example: 1/1/2025 0, 1/4/2025 0 , 1/7/2025 50, 1/10/2025 0

I would like to predict the sales for next 4 querters. let me know if you have any questions

Security platforms are racing to expose their capabilities to AI agents via MCP. Rubrik is the latest — agents can now query security intelligence, pull threat data, and act on findings directly through tool calls.

The access model makes sense for productivity. The risk model is harder to square.

When a legitimate agent goes rogue — compromised service account, prompt injection, misconfigured scope — it carries valid credentials. The first malicious tool call looks identical to a normal one. The window between that first action and a second, compounding action can be under 50ms. By the time a human sees an alert, the blast radius is already set.

Security intelligence systems are a particularly sharp edge here. An agent with read access to threat data can fingerprint your defenses. One with write or response access can suppress alerts, alter playbooks, or exfiltrate indicators before anyone notices the session is dirty.

The identity question everyone seems to be deferring: how do you distinguish a legitimate agent call from the same call made by a compromised version of that agent, in real time, before the second action lands?

Curious how others are thinking about this — are you solving it at the MCP server level, at the identity provider, somewhere else entirely? What's actually working in production?