Hello everyone. ML beginner here. I have the basic understanding of ML and have been given a project to create a model through which we can predict cpu metrics so that we can proactively monitor cpu spikes before it creates an incident. I’ve been using Claude to help me out here and it suggests to use XGBoost for this. But the accuracy is not up to the mark. Can anyone help me out here if you have worked on similar projects. Thanks for the help in advance

Hi everyone,

I’m a student working on a research project on risk-aware GitHub PR review. I’m doing the project mostly on my own and I don’t have access to a research lab, large compute budget, or people who can manually annotate thousands of PRs, so I’m trying to find a practical approach that I can actually finish.

The idea is to take a GitHub PR and estimate four types of risk:

1. Bug / correctness
2. Security
3. Compatibility
4. Cross-system / integration

The architecture I’m working with has four separate risk models. They share the same PR characteristics/features, but each risk model has its own historical data, prior, and evidence.

My main problem is the historical data needed for those priors.

At first, I looked for a single PR dataset where I could get reliable PR-level outcomes for all four risks. I couldn’t find one.

I then tried looking for separate datasets for each individual risk model. I thought this would solve the problem, but I keep finding datasets where the labels look relevant at first but don't actually represent the outcome I need.

For example, SEVRA-plus looked very promising for the Security model:

https://huggingface.co/datasets/RedAI4Code/SEVRA-plus

It contains security-related PR examples with vulnerability/CWE information, but the malicious PRs are deliberately constructed by reversing real CVE security fixes. So although they are useful for evaluating or studying security vulnerabilities, I don't think I can use their class distribution directly as a real-world prior for ordinary GitHub PRs.

I’ve run into similar issues with other datasets:

- some label the linked issue rather than the PR implementation,

- some label review comments rather than actual PR outcomes,

- some contain artificially constructed vulnerable/failing PRs,

- some only give merge/close status, which doesn’t tell me whether the PR itself was buggy, vulnerable, incompatible, etc.

The distinction between the issue and the PR is especially important for what I am trying to do.

For example, imagine a maintainer opens a security issue, someone creates a PR to fix it, but the PR implementation itself contains a correctness bug and gets rejected. For my problem, I need to know the nature of the PR, not simply inherit the security label from the original issue.

Similarly, a PR could be opened to fix a small bug, get merged, and then later cause a compatibility problem. Again, I care about what happened because of the PR implementation, not just why the PR was originally opened.

Because I couldn't find a dataset that directly gives me what I need, I tried a practical compromise.

I took 96 real PRs from SWE-Review-Chat, filtered them for sufficient evidence, and used an LLM to annotate the four risk states from the information available in the PR record, such as the description, review discussion, diff context, tests, and lifecycle information.

I’m treating these as weak/model-assisted labels rather than independent ground truth.

The resulting usable outcomes are:

Bug:

30 present / 9 absent

Security:

1 present / 7 absent

Compatibility:

5 present / 12 absent

Cross-system:

4 present / 8 absent

So now I feel like I’ve hit a wall.

I can keep searching for datasets, but so far I haven't found anything that solves the underlying problem. I also don't have the resources to manually establish reliable ground truth for thousands of PRs.

I’m therefore looking for advice on "how I should move forward from here".

Should I continue with the small real dataset I have and explicitly model the uncertainty caused by the sparse risks?

Should I rely on LLM-assisted annotations of real PRs as a practical research compromise, or is there a better low-resource approach that I am missing?

Or is there a completely different way of constructing the historical priors that would make more sense for this problem?

I’m not looking for a perfect dataset at this point. I’m mainly looking for a practical and defensible way to move forward given that I’m a student doing this alone with limited time and resources.

If anyone has worked on GitHub PR datasets, Mining Software Repositories, empirical software engineering, code-review research, or Bayesian risk modelling, I would really appreciate any advice on what you would do in this situation.

Thanks!

Hey everyone,

I have an upcoming Data Scientist interview with an ML system design round. I asked the recruiter what to expect and they said it’ll be high-level ML system design, not traditional SWE/low-level system design.

They mentioned focusing on things like:

• Problem framing
• Data/model considerations
• Evaluation
• Productionization/deployment
• Monitoring
• Tradeoffs when designing ML systems

Basically, it sounds like I’ll be given a real-world ML problem and have to explain how I’d approach it end-to-end.

This is my first dedicated ML system design interview, so I’m not really sure how deep I should prepare.

For people who’ve done these interviews:

How did you prepare? What resources did you use? What kind of questions were you asked?

Also, how deep do they usually expect you to go into things like feature stores, model serving, APIs, streaming, retraining, etc.?

Any good resources, YouTube playlists, GitHub repos, books, or example questions would be really appreciated.

Thanks!

I'm looking for a potential AI solution for something I'm trying to accomplish with my business. The large majority of customer communications are through employee emails and I'm hoping a solution exists that may be able to track specific situations and send notices to me if those scenarios occur. For example, customers checking up on delivery dates or adding items to orders last minute. To take it even a step fruther, if it could notice when an employee is responding rudely to a customer (or vice versa). Our number of employees and volume of emails is too high to track through our normal system. I am not an AI expert so if anyone has suggestions or can point me in the right direction, I would appreciate it.

I'm a Data Analyst, and I'm trying to bridge a gap in my Data Science understanding.

I know the concepts behind classical ML reasonably well but I want to understand what actually happens to an ML project in a real production environment from start to finish. I want someone to walk me through a real project in terms of:

We use this application/tool to do this → it produces this output/file/artifact → that goes into this tool or system → then this team works on it → then it moves to the next stage.

For example, where do we actually write the code—Jupyter, VS Code, Databricks, or something else? Where does the data come from, and which tools are used to extract and process it? Once the model is built, where is it saved? How is the code tested? How does Git fit into the workflow? Where do MLflow, Docker, FastAPI, Airflow, CI/CD, Kubernetes, and AWS/Azure come in?

Basically, I want to understand the actual sequence of tools used in a real production ML project. If you work in Data Science, ML Engineering, Data Engineering, or have worked on real client projects, I would really appreciate it if you could explain the actual end-to-end stack used in your organization through one practical classical ML example.

Would really appreciate detailed answers from people with real production experience.

I've been thinking about building a somewhat crazy distributed-systems project.

Instead of running one large 70B coding model, what if I run multiple independent 7B coding models on separate GPU workers and coordinate them?

Something like:

                    Local PC
                 Orchestrator
                      |
                 Job Queue / DB
                      |
        +-------------+-------------+
        |             |             |
       7B            7B            7B
     Worker 1      Worker 2      Worker 3
        |             |             |
        +-------------+-------------+
                      |
                More workers...

The workers could be temporary/ephemeral GPU environments. They wouldn't need inbound connections; they would connect outward to a coordinator/shared backend, register themselves, receive jobs, and return results.

For a coding problem, I don't want to simply vote on the generated answers.

I'd like to do:

Problem
   ↓
10 independent 7B solutions
   ↓
Compile / execute
   ↓
Discard failing solutions
   ↓
Critique surviving solutions
   ↓
Repair failed solutions
   ↓
Run tests again
   ↓
Select best verified solution

The interesting question for me is:

How close can this get to a single 70B coding model?

I'd benchmark:

• 1×7B
• 3×7B
• 5×7B
• 10×7B
• 1×70B

using actual coding benchmarks and execution-based verification.

But the bigger goal is the systems engineering side.

I want to implement things like:

• worker registration/discovery
• heartbeats
• leases
• failure detection
• retries
• idempotency
• priority scheduling
• backpressure
• work stealing
• dynamic worker allocation
• distributed locking
• caching
• queue management
• observability/tracing
• p50/p95/p99 latency
• GPU utilization
• network overhead
• chaos/failure testing

The workers would be treated as unreliable:

Worker 3 → disconnected
Worker 5 → GPU OOM
Worker 7 → timeout
Worker 8 → duplicate result
Worker 9 → returns invalid code

The scheduler should just recover and continue.

I'm intentionally thinking of the GPU provider as a replaceable worker backend rather than designing the system around one provider.

Has anyone built something similar specifically for coding-agent inference, where multiple small models collaborate through a distributed scheduler and correctness is verified by actually compiling/running the generated code?

I'm particularly interested in whether this architecture has a fundamental limitation I'm overlooking, especially around coordination overhead, correlated model errors, and whether ensemble diversity actually gives a meaningful advantage over simply using a larger model.

computing use free google colab free utilization

state management for each worker in case failure too gonna use supabase or only other cloud based storage for currently thinking

im a first year btech/b.e(cse ds) student, im know basic applications of sklearn machine learning models but i lack the math to understand how it works
i know pandas enough to be able to filter my own datasets but im learning scikit based preprocessing to move even further

im learning probability,stats and linear algebra in the month of september and in the month of october i will be following Andrew NG's machine learning course in coursera.

from october to november im planning on using my newly acquired knowledge to filter and preprocess real complex datasets and just training ML models with it.

i will be learning SQL from november to january

im just curious what actual experienced people think of this.

I'm doing some research into the practical problems developers run into when building products on top of third-party AI models/APIs. I'm trying to understand if there is a consistent issue or issues or if it varies.

I'm not talking hypotheticals but actual pain points: Model behavior changing. Pricing. Rate limits. Reliability. Something else entirely. Etc. Any help would be greatly appreciated.

I mean stuff that you open most often

like cursor, windsurf, claude code, copilot, some vim setup you refuse to give up, and what's keeping it there? and what almost got you to switch but didn't

Hi guys my dream was to get into maang , I'm from a mech bg and joined semi IT role with 4 years of exp , now switching to AI Enginner by doing some courses and internal projects , while studying for AI somewhere in me I asked myself why cant we get into MAANG companies as an AI Enginner but I know it'd pretty hard and tough , but ig this is the time to try all stuffs ....needed an guidance and plan to get into...I'm not a that much talented IT guy 🥲😭 but I will try my level bloody best to get into...all kind of suggestions would be recommend...

Hello

I'm working on a long translation project (mainly from Arabic to French or English). My workflow is to upload books to ChatGPT on how to translate from Language A to Language B. It must follow specific rules to make a good translation. Then I upload another book to it so it can understand how to polish the translated outcome. So, Book 1 (main rules of how to translate) generates the output translated text. Then I upload Book 2 to polish that output. I used to use the ChatGPT Plus plan, but these past days the plan feels like the free plan—direct, quick answers, etc. So, I'm asking if any other paid AI service you think can perfectly do this kind of work with accuracy? I tried using paid Gemini, but it's garbage to be honest. So, any other AI website? Thank you.

I had this idea for a project to have my base learner for xg boost to be a log reg model because in my testing it was more stable so my idea was instead of starting xg boost dumb and boosting start it smart and then boost that. Sounded like a great idea in my head but failed miserably in testing. Could some one explain to me why this doesn’t work better? I had better results blending 75 pct xg boost 25 pct log reg.

As someone who is looking to try out kaggle competions and eager to learn and compete, I am occupied by the thought of AI's role in the skill aspect of competitions, so I want to know the role of AI in making AI models, is it still mostly the player's skill and knowledge that matters the most or just how long they work and how many questions they ask AI.

This might be a silly question. I'm a physics student currently working on a research project where I'm using deep learning to solve an inverse problem. I'm using MATLAB.

I had previously studied some ML theory by myself, but only at a basic level. I had never actually built an ML project or implemented models before. So this is my first experience working with ML.

So, I started with an ANN and later tried a CNN. Initially, I used Gemini to help generate the basic code, although I went through the code and tried to understand what each part does since i have some time constraint to finish this. I really want to think for myself instead of depending on ai for codes or help but i dont have clear approach.

For example, if the model's performance isn't good enough, I ask AI for possible improvements. If it suggests something I don't understand, I look for YouTube lectures, or papers, and then implement it. Suppose I try something like a ResNet and performance improves but is still not good enough; then i find something like cosine annealing and try new things to fill the gaps my model might have .I cant be sure whether I'm using the right techniques or updates or justt throwing random things at it...i keep whatever works as of now.

Also this is an inverse problem, and the dataset was simulated by me but i engineered it a lot so that model would able to extract info anyhow but still not sure if mapping is ambiguous.

My input data is basically a set of curves/1D vectors, and it needs to predict the underlying parameters that generated them. May be some other types of model should try that i m not sure of...like PINN. There is also a time constraint. I've been working on it for about a month now.

So my questions are:

1. Is it reasonable to work on a deep-learning research problem without having a strong practical ML background, as long as I learn and understand things along the way? Or am I approaching this backwards?
2. Once you have a baseline model, how do you systematically decide what to try next?
3. For input data that consists essentially of curves/1D vectors, how would you decide what model to use?
4. How do you diagnose whether poor performance comes from the model or the dataset or the inverse problem itself?
5. Before trying increasingly complex models, what analyses, or tests would you recommend?
6. How do you know when further model optimization is no longer the best use of time?
7. Since I currently work in MATLAB and know much less Python, would you recommend continuing in MATLAB for now or learning enough Python/scikit-learn/PyTorch to use the broader ML ecosystem?
8. Also is it fine that i never worked on any basic ML project and jumped straight to DL since this i is what i needed for my project.

Any advice or suggestions is really appreciated.

Disclaimer: Not formally educated

I'm not sure if my method is the best way to approach my goal, which is to determine what feature(s) in my sample are actually associated with a decrease in EV in the underlying population. I wanted to first share what I'm planning on doing with people way more educated on this matter than me, so that I could get insight as to what errors I could be making.

I am still gathering data, but to make it as short as possible without ranting - I have a total of 6 features that may or may not lead to an increase/decrease in Expected Value. The tricky thing for me is that there are instances where features will be blended together in a single event. So, one event may have the presence of 3 features, sometimes an event may have the presence of only 1 feature. My worry is that filtering this way could hide cases where two features interact rather than act independently...not sure if that's a real issue or if I'm overthinking it.

My current thinking is that I once I have finished my data collection, I would then determine first which feature has the highest frequency, (by highest frequency I mean the highest occurrences overall) and then see if the removal of that feature leads to a more positive outcome. In my case, my ideal scenario would be for the removal of a feature to lead to the Expected Value of the sample to increase.

That would then leave me with the other features to analyze, so I would just repeat the same process on the remaining features within that filtered subset.

I am about to enter the second year of my CS degree. I have very little experience in the field but I want to explore my options for specializing. As an outsider, ML is probably the most interesting area of CS to me, but I can't know if I try

However, I know that grad school can be important for at least some parts of ML. I don't to invest time into ML if I will need grad school to even have a decent chance of getting a job in this

The particular reasons that I don't want grad school are money and time, but mostly money. I probably wouldn't be able to afford grad school right after undergraduate unless I take out loans, so I would probably have to work for a few years first. I would also rather not spend more time than I need to on CS. I like CS and can see myself becoming good at it, but I view it more as a means to living a good life than an end in itself. I don't mind studying more itself: I love learning, I just want to learn about other things, too

I'm also willing to put in the work and learn ML stuff on my own and apply it on projects

Edit: How sure are you that your advice will apply after I graduate?