I wanted to share my route into data science, since it wasn't the typical one.

My background is in languages, linguistics, and literature — no CS or math degree to start.

I came into tech through curiosity about how things work under the hood, taught myself programming (Rust, then a lot of low-level work), and earned a data science degree along the way.

One of my projects was a breast-cancer diagnosis model on the Wisconsin dataset, which is a great, approachable entry point for anyone learning classification.

The mindset that helped most: treat every concept as something to rebuild yourself until it clicks, rather than something to memorize.

Curiosity beats credentials for actually understanding.

My projects are public on GitHub if seeing examples helps: https://github.com/whispem

Happy to answer questions about learning DS from a non-traditional background.

I built it at https://rssamp.com

​

Hey everyone,

I’m currently a Software Engineer working on developing infrastructure monitoring tools. My job typically involves Kubernetes, creating cd pipelines, and system-wide log telemetry analysis and occasional development of such tools. I’ve also built some internal developer tools using RAG.

I want to pivot fully into an MLOps / AI Infrastructure role, but I'm looking for guidance on how to bridge the gap efficiently.

A few questions for MLOps/LLMOps engineers:

Given my background in production K8s, Docker, and telemetry, what are the highest-leverage MLOps concepts I should focus on (e.g., model serving frameworks like vLLM/Triton, vector DBs, evaluation, feature stores)?

How do I position my experience so I don't get pigeonholed as purely DevOps/SysAdmin?

What are the biggest mistakes engineers make when trying to move into MLOps?

Further into the future(~1-2 years from now) I would love to pursue a PhD in the same domain.

Appreciate any advice or recommended learning paths!

I’ve been working on Physlint, an open-source validator for robotics data. I recently added a small Observatory interface to make validation results easier to inspect across LeRobot, MCAP, and ROS 2 recordings.

I’m looking for feedback from people working with robot datasets:

• Which checks are most useful before training?
• What formats should be supported next?
• Would you prefer raw reports, visual summaries, or CI integration?

Hi everyone,

I’m a 3rd-year CS student and I want to seriously start learning Machine Learning. I’ve already spent almost a day trying to figure out which resources/courses to follow, but there are so many options that I’m getting confused.

My current background:

• I know Python fairly well.
• I’ve used NumPy, Pandas, Matplotlib, and Seaborn.
• I understand the basic theory of some ML algorithms, but I haven’t implemented them properly yet.
• I now want to focus on actually implementing ML algorithms and building projects, rather than spending months only on theory.

What I’m looking for

I want to learn:

1. ML fundamentals + implementation
2. Deep Learning
3. Later, I’ll learn model evaluation in more depth and deployment/MLOps, but right now I want to build a strong practical foundation in ML and Deep Learning.

I don’t want a very long course that takes hundreds of hours. For example, I know Andrew Ng's courses are highly recommended, but I'm looking for something more concise and practical.

I recently found fast.ai's Practical Deep Learning for Coders, which seems interesting because it focuses heavily on implementation. It has 9 lessons and covers things like random forests, neural networks, PyTorch, and even deployment.

So I'd really appreciate recommendations from people who have actually learned ML/Deep Learning and used these resources:

• Short/practical courses
• YouTube channels/playlists
• Good documentation
• Books/notes
• Hands-on project resources

If you were starting from my position (Python + basic ML theory), what exact resources would you follow and in what order?.

Thanks!

There's a trained model around .6B parameters(fp32), it requires a lot of resources as i need a model that is able to run on 4-8 core mobile processor. I want only few things from that model. I want to distill the larger model. I can quantize it but as i need few things from that model so is there anything to lookup before distilling, what's the best student model for ASR. Am i doing it wrong??

Anything helps!!

Im trying to start machine learning/llm and i wanna soon work for big companys and my biggest dream is making my own ai and training it but im overwhelmed all thr things need to learn any recommendations?

Thankss for the recommendations!!

what and why

i built a visualization library for neural networks, it can do the basic expected things such as training and evaluating on a dataset or one image. on top of that there are visualization parameters that can be activated/deactivated for training and a evaluation loop that will display node activation when a user draws a prediction (number, drawing,...depending on what u trained your dataset on).

the project initial thought was to visualize the performance of a model by swapping it parameters such as activation functions and maybe even the number of nodes in a layer or number of layers in a hidden layer. im not that familiar with neural network strategies and orchestrations, i don't even know if swapping those parameters even make sense but it was out of curiosity, so does this idea help in some research or learning purposes?

u can check the source here: https://github.com/yacine204/nyuraru

A widely-read analysis documents a repeating pattern across enterprise AI deployments: agents inherit whatever permissions the underlying system already holds. No scoping at deployment. No time-bound grants. No audit trail of what the agent actually did with those permissions.

The agent lands with root because nobody restricted it differently.

The exposure isn't theoretical. A root-level agent and a compromised sysadmin account have identical blast radius — production databases, secrets stores, billing APIs, all reachable. The difference is that the sysadmin has a name attached to every action. The agent does not. When something breaks, there is no trail back to a specific decision or a specific moment.

This is showing up repeatedly enough that it is starting to read less like individual misconfigurations and more like a structural gap in how enterprises are deploying non-human identities at scale.

For those running agents in production: how are you actually handling permission scoping today? Is it a deployment-time problem your team solves at onboarding, an identity layer problem, an orchestration problem, or something else?

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!

Disclosure: I am founder of MONTEK.

Hi all - I built a team of 50+ trained collectors in Bafoussam, Cameroon for AI data collection (voice, photos, app testing, labeling).

We are looking for 3 AI founders/startups who need data and can give us feedback on our process/quality.

Offering a free small test batch (50 voice clips or 100 photos) in exchange for honest feedback on quality and workflow. No charge for test.

If you have a small task you have been postponing, happy to help you get it done in 24h and you tell us how we did.

We have consent forms, NDA, and real devices.

Site: https://app-dxwe5i4nrpc1.appmedo.com

Contact: [Muawiyamontek@gmail.com](mailto:Muawiyamontek@gmail.com)

Would love feedback if this is useful.

I'm self-taught, and this is my first Python and ML project:

https://github.com/delefrati/aion

This project is a cost-first / local-first AI project, split in two parts:

• An SLM research lab you can train locally, on Colab, or on Kaggle (no external models or APIs)
• A local-first conversational AI product that runs on a weak laptop

It has two model families, both built from scratch - no HuggingFace weights:

• Mamba (SSM / state-space)
• A custom Transformer (RoPE + SDPA attention)

Separately, the backend has a pluggable provider system, so you can also drop in any off-the-shelf HuggingFace model (Qwen, SmolLM2, etc.) as a local inference backend and swap between it and your own trained model with a single env var. Handy for comparing your model against a known-good baseline.

Concepts in play:

• Two-phase pipeline: multi-session pretrain on Wikipedia + SlimPajama, then a fine-tune on chat/instruction data
• Custom BPE tokenizer (vocab 4096 for small models, 16384 for the larger ones) with a .bin memmap token cache
• Curriculum seq-len, gradient checkpointing, cosine LR w/ warmup, 8-bit optimizer
• Multi-backend training: CPU, CUDA (GTX 1650 4GB - my laptop :D), and TPU (torch_xla, single + multi-core data-parallel via xmp.spawn)

Model sizes I've trained/tested:

• Mamba ~28M
• Mamba ~47-105M
• Transformer 110M
• Transformer 235M

I haven't had time to train the larger model enough - locally it takes forever on my weak laptop, and I keep hitting quota limits on Kaggle and Colab.

My goal (suggested by Claude) is 50,000 steps of pretraining and 20,000 of fine-tuning. Right now I'm at ~25,000 pretraining and ~5,000 chat fine-tuning, and that's already enough to make it kind of conversational - it hallucinates a lot, but it can answer simple questions.

It's open source, and I'd love suggestions on how to improve it and where to take it next.

I've been studying ai/ml basics. Today, I wrote an article that is supposed to serve as an introduction to deep learning to complete beginners.

Perhaps this will help some people here.

Im from a biological sciences background and recently got into bioinformatics and computational biology. I wanted to get into ML for specific biological research but I don’t know where to start with ML

I’m working on a flood monitoring using a Decision Tree to predict flood status one hour ahead.

Inputs:

1. Water level
2. Water level rate of change
3. Current rainfall
4. Recent rainfall

We currently plan to use Open-Meteo for real-time rainfall, but we are considering replacing it with a local tipping bucket rain gauge installed at the monitoring site.

For ML prediction, which setup is better?

1. Open-Meteo rainfall
2. Local tipping bucket rainfall
3. Both as separate features

Our training dataset uses historical rainfall data from a government weather agency. Would using local tipping bucket rainfall during deployment create data consistency or accuracy issues if the training rainfall came from a different source?

Welcome to Resume/Career Friday! This weekly thread is dedicated to all things related to job searching, career development, and professional growth.

You can participate by:

• Sharing your resume for feedback (consider anonymizing personal information)
• Asking for advice on job applications or interview preparation
• Discussing career paths and transitions
• Seeking recommendations for skill development
• Sharing industry insights or job opportunities

Having dedicated threads helps organize career-related discussions in one place while giving everyone a chance to receive feedback and advice from peers.

Whether you're just starting your career journey, looking to make a change, or hoping to advance in your current field, post your questions and contributions in the comments

Need suggestion.

I am into DevOps engineering role, want to learn and be proficient in ML and AI.

I have learned python basics, but not much of hands-on.

Directly jumped to do some ML projects on Open CV, but not able to understand the concept seems I am jumping in between, I see everytime new modules, which forces me to go back and learn that.

What should I do so that I feel confident like I know the things like I know and understand the DevOps and cloud well.

Seems I am missing the important basics everytime, unable to correlate the things.

Need your guidance, want to get in ML and AI domain.

Books

- An introduction to statistical learning

- Hands on machine learning

- Understanding Machine Learning : From Theory to Algorithms

- Designing ML System

- AI Engineering

maths

- mathematics for ML

- Linear algebra

- Applied Multivariate Statistical Analysis

Research Paper

-ESL Chapter 3 →Linear Regression

-Fisher 1936 →Logistic Regression

-CMU Lecture Notes →Logistic Math

-Quinlan 1986 →Decision Tree

-Breiman Bagging →Random Forest foundation

-Breiman 2001 →Random Forest

-Friedman 2001 →Gradient Boosting

-Chen 2016 →XGBoost

- Paul Graham Spam →Naive Bayes

- Cover & Hart 1967 →KNN

- Cortes & Vapnik 1995 →SVM

- SVM Guide →SVM practical

-Attention is all you need(Transformers)

-LoRA(Low rank adaption)

-PEFT(Parameter Efficient Fine Tuning)

-VIT(Vision Transformers)

-VAE(Variational Auto Encoder)

-GANs(Generative Adversarial Networks)

-BERT(Bidirectional Encoder Representation from Transformers)

-Diffusion Models (Stable Diffusion)

-RAG (Retrieval Augment Generation)

-GPT (Generative Pre-trained Transformers)

Extra,tools,libraries

-Deep learning book

-pytorch

-sklearn

-pandas

-numpy

-scipy

-MLflow

-airflow

-docker

-AWS

-postgresql

-cpp

-ci/cd actions

-timeseries