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r/learnmachinelearning

LLM costs and prompt leaks turned out to be bigger problems than I expected
▲ 27 r/cybersecurity+5 crossposts

LLM costs and prompt leaks turned out to be bigger problems than I expected

Been working on something recently and wanted a sanity check from people here.
While building with LLM APIs, I kept running into two things:

- costs getting kind of unpredictable depending on which model/provider was used  

- people pasting sensitive stuff into prompts without really thinking about it  
So I started putting a thin layer in front of the requests to catch obvious sensitive data before it leaves and route requests to cheaper/faster models when possible  

Nothing too fancy, just trying to solve the same issues I kept hitting. https://opensourceaihub.ai/

u/Bootes-sphere — 17 hours ago
▲ 8 r/learnmachinelearning

Beginner roadmap for Anthropic’s free courses: What’s the best order and cost?

I want to start the free AI courses provided by Anthropic

as a total beginner in the field, I don't know what's the best order to take the several courses there.

I’m also trying to figure out the most cost-effective way to follow along. The courses themselves are free, but using the actual Claude Code interface or certain developer tools requires a paid subscription or API credits.

Can I complete the learning paths for free with some workaround? Or is it necessary to put a minimum amount of credits into the Anthropic Console to actually do the labs?

Any guidance on a path that won't hit a major paywall halfway through would be great.

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u/Prestigious_Guava_33 — 4 hours ago
▲ 3 r/learnmachinelearning+1 crossposts

ML training platform suggestion.

Working on my research paper on vehicle classification and image detection and have to train the model on YOLOv26m , my system(rtx3060 ,i7, 6 Gb graphics card and 16Gb RAM) is just not built for it , the dataset itself touches around 50-60 gb .
I'm running 150 epochs on it and one epoch is taking around 30ish min. on image size which i degraded from 1280px to 600px cause of the system restrains .

Is there any way to train it faster or anyone experiences in this could contribute a little help to it please.

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u/Ehsan-Khalifa — 2 hours ago
My neural network is getting better (accuracy tracking) – Day 8/30 & i discover a new networking
▲ 7 r/learnmachinelearning+1 crossposts

My neural network is getting better (accuracy tracking) – Day 8/30 & i discover a new networking

Day 7 of building a neural network from scratch in Python (no libraries). & i discover a new networking

were i want to give it as open source were i even made report if you need use it i am do inside of reddit because i don't have a git account

the image above seeing was the simulation of the network 🙄🙄🙄

i well explain this new network in other post that was reson for delay of post

Today I started tracking accuracy.

Until now, I knew the model was learning because the loss was decreasing.

But accuracy makes it clearer:

How often is the model actually correct?

Right now, the accuracy is still low — but it’s improving with each training cycle.

Example:

Epoch 1 → Accuracy: 12%

Epoch 3 → Accuracy: 28%

Epoch 5 → Accuracy: 41%

This might not look impressive yet, but it proves something important:

The model is learning.

Each iteration makes it slightly better than before.

Tomorrow, I’ll focus on improving performance and making training more efficient.

Day 8/30 ✅

I’ll update again tomorrow.

u/elonkingo — 4 hours ago
▲ 7 r/learnmachinelearning+1 crossposts

I am currently work in bpo and want to become ai engineer, i also make ivr systum and email sender and replyer automation by using ai. Can i switch to it from non it degree

Hi everyone,

​I need some brutal honesty and guidance from the folks working in the AI/Tech industry.

​My Current Situation:

I am currently working in the BPO/Customer Support sector and I come from a completely non-IT academic background (Arts/English). I know that on paper, my resume gets auto-rejected by 99% of ATS systems for any engineering role.

​What I am doing about it (Proof of Work):

I didn't want to get stuck in the BPO trap, so I started focusing heavily on Applied AI and automation during my off-hours. Instead of just doing tutorial projects, I built practical tools:

​AI-Driven IVR / Outbound Calling Bot: I built an automated 24x7 AI calling agent designed to help small businesses handle outbound queries using APIs (LLMs + Twilio/voice APIs).

​AI Email Automation: A system that reads, categorizes, and automatically replies to emails using AI.

​I am also currently completing my Google IT Support Professional Certificate to ensure my foundational knowledge of networking and systems is solid.

​My Goal:

I don't want to be a core ML researcher building models from scratch (I know I lack the advanced math/CS background for that right now). My goal is Applied AI / AI Automation Specialist / Cloud Operations—basically integrating existing AI models into business workflows.

​My Questions for the community:

​Will my "Proof of Work" (these live AI projects) be enough to bypass the strict B.Tech/BCA degree filters in Indian startups?

​Should I focus on getting Cloud certs (AWS/Azure) next, or double down on things like LangChain / Python scripting for my portfolio?

​How should I approach founders or HRs directly to showcase my projects since standard portal applications won't work for me?

​Any advice, roadmap tweaks, or reality checks are highly appreciated. Thanks!

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u/Great-Illustrator571 — 3 days ago
▲ 2 r/learnmachinelearning

Fraud detection vs medical vs LLM

Need help with choosing a field to do research on asap 😭 So I’m joining a lab at my uni. And upon application, I need to choose a specific field to follow. Initally, my top choice was fraud detection but ppl in the lab said that it was really hard and a lot of pure math involved. That really scared me so I’m thinking of switching to maybe AI in medical field or LLM. Please give your opinion and help me choose! Thank you!

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u/thegreatestrang — 2 hours ago
[P] I built an AI framework with a real nervous system (17 biological principles) instead of an orchestrator — inspired by a 1999 book about how geniuses think
▲ 2 r/learnmachinelearning+1 crossposts

[P] I built an AI framework with a real nervous system (17 biological principles) instead of an orchestrator — inspired by a 1999 book about how geniuses think

I'm a CS sophomore who read "Sparks of Genius" (Root-Bernstein, 1999) — a book about the 13 thinking tools shared by Einstein, Picasso, da Vinci, and Feynman.

I turned those 13 tools into AI agent primitives, and replaced the standard orchestrator with a nervous system based on real neuroscience:

- Threshold firing (signals accumulate → fire → reset, like real neurons)

- Habituation (repeated patterns auto-dampen)

- Hebbian plasticity ("fire together, wire together" between tools)

- Lateral inhibition (tools compete, most relevant wins)

- Homeostasis (overactive tools auto-inhibited)

- Autonomic modes (sympathetic=explore, parasympathetic=integrate)

- 11 more biological principles

No conductor. Tools sense shared state and self-coordinate — like a starfish (no brain, 5 arms coordinate through local rules).

What it does: Give it a goal + any data → it observes, finds patterns, abstracts to core principles (Picasso Bull method), draws structural analogies, builds a cardboard model, and synthesizes.

Demo: I analyzed the Claude Code source leak (3 blog posts). It extracted 3 architecture laws with analogies to the Maginot Line and Chernobyl reactor design.

**What no other framework has:**

- 17 biological nervous system principles (LangGraph: 0, CrewAI: 0, AutoGPT: 0)

- Picasso Bull abstraction (progressively remove non-essential until essence remains)

- Absent pattern detection (what's MISSING is often the strongest signal)

- Sleep/consolidation between rounds (like real sleep — prune noise, strengthen connections)

- Evolution loop (AutoAgent-style: mutate → benchmark → keep/rollback)

Built entirely with Claude Code. No human wrote a single line.

GitHub: https://github.com/PROVE1352/cognitive-sparks

Happy to answer questions about the neuroscience mapping or the architecture.

u/RadiantTurnover24 — 19 hours ago
▲ 1 r/learnmachinelearning

Best Machine Learning Prediction System Github Repos?

currently creating a baccarat prediction system (yes I know it's impossible) but I'm doing it for the heck of it and because it's hard, profiting from it would be a side bonus, only did it to make daddy Nietzsche proud by attempting the great and the impossible.

is there any actual good github repos that has prediction systems I can take a look on? one that applies quant trading (stochastic markov chain and whatnot) incremental training, randomforest, xgboost, monte carlo simulators and so on that y'all think is worth taking a look? .

for the boring part:

what I did!!!

initially I wanted to predict something, coin toss is....actually impossible, dice rolls are impossible so next on the list is cards, but I needed to attach a theme onto it and how it behaves rather than pulling cards from it one by one and I was introduced with Baccarat since there is a specific ruleset and you only have to predict left or right, red or blue.

what I did was that I attached 16 currently existing prediction system each have their own rules

"always bet P B P B"

"always bet P P B B"

"always bet on the recent winner"

"always bet on the...."

theres so many and some aren't as basic as the first two...I gott hem all from youtube and observation (watching them on twitch)

now they are indicators, what's next is that I made a machine learning model that detects when they were right and wrong, detecting their behavior and pattern, when were they correct, and when they were wrong, since basically baccarat is at the mercy of the shuffle of the shoe (8 decks per shoe) and then I made a monte carlo simulator that has those 16 prediction system betting on it so that I can simulate the game rather than watch it on twitch for lengthy amounts of time.

i made three apps, monte carlo simulator, the ml trainer, and the baccarat app that can import the ml model and provide it's predictions

the ml trainer provides two models, the gatekeeper and the primary, gatekeeper says when it is confident to bet, while primary is the one that says P or B

currently the loop is that I create data from a monte carlo simulator, then import it to create a model in the trainer, import it back to monte carlo simulator to play and lose and learn from its mistakes and so on and so forth, then back to trainer.

I use entropy targeting to measure the randomness in the data, feature locking for data that doesn't contribute to anything, and l1 and l2. it also has gradient descent, sigmoid scaling, and markov chain.

so currently the question would be am I doing the stuff correctly or am I executing it correctly which is why I am deep diving into github repos to check actual works since I've only been doing this on my spare time so around two weeks worth with 5 hours a day

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u/jjustineee — 1 hour ago
▲ 1 r/learnmachinelearning

How to estimate an objects distance?

I know there's models like DepthAnything or VGGT, but the problem is they don't have semantic understanding. I was thinking of combining a model like YOLO to get an object bounding box then using a depth model, but you can't know where within the bounding box to take the depth, as often theres background or occlusions within the box that aren't the real object. Anyone know a good way of doing this?

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u/boringblobking — 1 hour ago
From 17 node types to 6: my 11-step GraphRAG pipeline, what worked, and what's still broken
▲ 1 r/learnmachinelearning

From 17 node types to 6: my 11-step GraphRAG pipeline, what worked, and what's still broken

While building a financial assistant for an SF start-up, we learned that AI frameworks add complexity without value. When I started building a personal assistant with GraphRAG, I carried that lesson but still tried LangChain's MongoDBGraphStore. It gave me a working knowledge graph in 10 minutes.

Then I looked at the data. I had 17 node types and 34 relationship types from just 5 documents, including three versions of "part of". GraphRAG is a data modeling problem, not a retrieval problem.

The attached diagram shows the full 11-step pipeline I ended up with. Here is a walkthrough of what you can learn from each step.

So basically, in steps 1 and 2 of the data pipeline, raw sources go through an Extract, Transform, Load (ETL) process. They land as documents in a MongoDB data warehouse. Each document stores the source type, URI, content, and metadata.

Then in step 3, we clean the documents and split them into token-bounded chunks. We started with 512 tokens with a 64-token overlap. Still, we have to run more tests on this.

The thing is, step 4 handles graph extraction. We defined a strict ontology. An ontology is just a formal contract defining exactly what categories and relationships exist in your data. We used 6 node types and 8 edge types. The LLM can only extract what this ontology allows.

For example, if it outputs a PERSON to TASK connection with an EXPERIENCED edge, the pipeline rejects it. EXPERIENCED must connect a PERSON to an EPISODE.

We also split LLM extraction from deterministic extraction. We create structural entries like Document or Chunk nodes without LLM calls.

Turns out, step 5 for normalization is the hardest part. We use a three-phase deduplication process. We do in-memory fuzzy matching, cross-document resolution against MongoDB, and edge remapping.

Anyway, in step 6, we batch embed the nodes. The system uses a mock for tests, Sentence Transformers for development, and the Voyage API for production.

Ultimately, in steps 7 and 8, nodes and edges are stored in a single MongoDB collection as unified memory. We use deterministic string IDs like "person:alice" to prevent duplicates. MongoDB handles documents, $vectorSearch, $text, and $graphLookup in one aggregation pipeline. The $graphLookup function natively traverses connected graph data directly in the database. You don't need Neo4j + Pinecone + Postgres for most agent use cases. A single database like MongoDB gets the job done really well. Through sharding, you can scale it up to a billion records.

To wrap it up, steps 9 through 11 cover retrieval. The agent calls tools through an MCP server. It uses search memory with hybrid vector, text, and graph expansion, alongside query memory for natural language to MongoDB aggregation. The agent also uses ingest tools to write back to the database for continual learning.

Here are a few things I am still struggling with and would love your opinion on:

  • How are you handling entity/relationship resolution across documents?
  • What helped you the most to optimize the extraction of entities/relationships using LLMs?
  • How do you keep embeddings in sync after graph updates?

Also, while building my personal assistant, I have been writing about this system on LinkedIn over the past few months. Here are the posts that go deeper into each piece:

P.S. I am also planning to open-source the full repo soon.

TL;DR: Frameworks create messy graphs. Define a strict ontology, extract deterministically where possible, use a unified database, and accept that entity resolution will be painful.

u/pauliusztin — 1 hour ago
▲ 1 r/learnmachinelearning+1 crossposts

The 90% Nobody Talks About

I built a multimodal GAN and deployed it on GCP Vertex AI.

The model took 2 weeks. Everything else took 5 months.

Here's the "everything else":

→ 3 weeks building a data preprocessing pipeline

→ 3 weeks refactoring code for Vertex AI's opinions on project structure

→ A 1 AM debugging session because GPU quota silently ran out

→ Days fighting a CUDA version mismatch between local dev and cloud

→ Building monitoring, logging, and deployment automation from scratch

We romanticize the model in ML. We show architectures and loss curves.

We don't show the Dockerfile debugging at midnight.

That's the 90%. And it's where the actual engineering happens.

Full story: [https://pateladitya.dev/blog/the-90-percent-nobody-talks-about\]

#MLOps #MachineLearning #GCP #VertexAI #Engineering

https://preview.redd.it/jeaud5du46tg1.png?width=1200&format=png&auto=webp&s=1efe8410e6524f7fe4c7f8b980ed0249d4dbe02f

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u/invincible_281 — 3 hours ago
▲ 1 r/learnmachinelearning

I built a diagnostic layer for PyTorch training

I built a tool that detected a training failure at step 19 — before 600 steps of compute were wasted.

Without it: PPL = 50,257 (model completely dead)

With intervention: PPL = 1,377

That's a 36× gap. Replicated 3/3 seeds.

It's called Thermoclaw. Open source, one line to add to any PyTorch loop.

While working on the EPTO optimiser research project I kept running into silent training failures, runs that looked fine on the loss curve but were quietly dying due to weight decay collapse. I couldn’t find a tool that told me why things were going wrong at a layer level.. so I built one. Thermoclaw ( name is awful I know) wraps any PyTorch optimiser and measures thermodynamic quantities per layer.

It’s early days for thermoclaw and it needs your help! Please get in touch via my git hub repo to inform me of any issues.

Huggingface.co/spaces/christophergardner-star/thermoclaw

github.com/christophergardner-star/Thermoclaw

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u/National_Control4101 — 3 hours ago
Has anyone successfully implemented AI for customer support?
▲ 13 r/learnmachinelearning

Has anyone successfully implemented AI for customer support?

B2B SaaS, team of 8. We've been drowning in the same 20 support tickets on repeat, billing questions, onboarding steps, basic how-tos. Our one support person was spending 80% of her time copy-pasting the same answers and was burnt out. Couldn't justify a second hire yet.

Spent about a month testing tools before pulling the trigger. The market is a mess, everything claims "80% ticket deflection" but half of them are just a GPT wrapper that searches your docs and calls it a day.

We went with Chatbase.co Here's the honest breakdown after about 3 months:

Setup was genuinely fast. Connected our help docs, uploaded some internal PDFs, pointed it at our pricing page. No dev involved. Previous tool we tried (Intercom) needed two weeks and pulled one of our engineers off other work.

First couple weeks were rough, but not because of the tool. The bot was giving patchy answers because our documentation was all over the place. Spent a week cleaning up the help center and rewriting some SOPs, after that things got noticeably better. Classic garbage in garbage out situation.

After tuning we're sitting somewhere around 75% deflection on routine tickets. She still handles anything account-specific or emotionally charged, but the queue is actually manageable now.

Billing questions were the sticking point at first. The bot could answer general pricing stuff but couldn't touch anything account-specific. We set up the Stripe integration, it's native, took maybe 15-20 minutes and now the agent can pull invoice history and subscription status mid-conversation without handing off to a human.

A few things I wish someone had told us going in:

Clean your docs before you do anything else. Seriously, we skipped this step and wasted two weeks wondering why the bot was giving vague answers.

Don't go fully autonomous on day one. We ran it in a kind of review mode for the first two weeks where she could see every response before it went out. Caught a few edge cases early that would have been embarrassing with customers.

The handoff matters more than people think. If the bot just says "I can't help with that" and stops, customers get annoyed fast. Having a clear escalation path set up from the start made a big difference.

Anyone else gone through this? Curious what deflection rates other people are actually seeing after a few months, not the numbers on the landing page.B2B SaaS, team of 8. We've been drowning in the same 20 support tickets on repeat, billing questions, onboarding steps, basic how-tos. Our one support person was spending 80% of her time copy-pasting the same answers and was burnt out. Couldn't justify a second hire yet.

u/Grouchy_Subject_2777 — 22 hours ago
▲ 1 r/learnmachinelearning

What type of recommendation is appropriate?

Subject: Seeking insights on Recommendation Systems for diverse consumer products (Coffee, Perfumes, Cosmetics, Groceries, Personal Care, Nutritional Supplements, Cleaning Products)

Hey Reddit,

I'm working on recommendation systems and have 8 distinct product categories I'm focusing on. I'm looking for practical advice and personal experiences regarding the most effective recommendation strategies for each of these consumer product types:

* **Coffee**

* **Perfumes**

* **Cosmetics**

* **Groceries**

* **Personal Care Products**

* **Nutritional Supplements**

* **Cleaning Products**

Specifically, I'm interested in:

  1. **What type of recommendation system (e.g., collaborative filtering, content-based, hybrid, matrix factorization, deep learning-based, etc.) has yielded the best tangible results for each of these product categories in your experience?** I'm hoping for insights based on real-world implementation and measurable outcomes.

  2. **Has anyone successfully implemented and seen positive results from "context-aware" or "state-based" recommendations for any of these product types?** (By "state-based" I mean recommendations that adapt based on the user's current situation, mood, time of day, inventory levels, or other dynamic factors, often seen in content recommendation but curious about its application in physical products).

I'm eager to learn from your personal experiences and expertise in the field. Any detailed examples or case studies would be incredibly helpful!

Thanks in advance!

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u/No_Dot4335 — 4 hours ago
▲ 10 r/learnmachinelearning

Breaking into ML - what's required

Well, it seems like I'm perptually stuck in CS roles. 10 years in AV at a large company but it's folded. Not terribly thrilled with SWE at the moment in the current company, mostly all plumbing, integration, glue, very little in the way of algo dev. I have a MS CS with a ML specilaization. ~ 3 years ago. I really like math. Back prop math is fairly easy - albeit, I think architecture is more the the key. Yes, I recognize "plumbing, integration, glue" exists in MLE too.

"To break the narrative" do I just create portfolios to demonstrate proficiency? But won't ATS just throw my resume in the garbage as I've not had demonstrated ML work?

I have to imagine there's a "move to ML" or "ML career" FAQ somewhere.

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u/NearbyAntelope1413 — 21 hours ago
▲ 3 r/learnmachinelearning

Anyone tips for review author response period?

Hi, I submitted to IJCAI26 special track, and the author response period is close.
Anyone have any tips about rebuttal/ author response?

This is my first submission to conference.

Any of the tips would be so much valuable for me. Thanks!

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u/Bulky-Quarter-3461 — 10 hours ago
▲ 1 r/learnmachinelearning

Suggest me a youtube playlist for ML Coding

I've been working on the fundamentals and basics of ML and Deep Learning. Now, I think its the right time to start coding.

Please help me find a good playlist on YouTube.

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u/Flat-Technician5561 — 5 hours ago
▲ 3 r/learnmachinelearning

Looking for a simple end-to-end Responsible AI project idea (privacy, safety, etc.)

Hey everyone,

I’m trying to get hands-on experience with Responsible AI (things like privacy, fairness, safety), and I’m looking for a small, end-to-end project to work on.

I’m not looking for anything too complex—just something practical that helps me understand the key ideas and workflow.

Do you have any suggestions? Or good places where I can find Responsible AI projects? Thank you

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u/Designer_Grocery2732 — 11 hours ago
Week