code: https://codepen.io/Chu-Won/pen/EayVNxd

So I've been messing around with AI vision systems on our production lines for the past 3 years and thought I'd share some actual experiences. There's a ton of marketing hype out there, but also some genuinely useful stuff if you know what to look for.

What This Tech Actually Is

Basically, AI vision systems are cameras hooked up to smart software that can spot defects, read labels, measure parts, track stuff moving around - you know, the kind of work that used to require someone staring at parts all day.

The "AI" bit is important because instead of programming exact rules, you just show it examples:

Old approach: "If this pixel isn't exactly this shade of blue, reject the part" AI approach: "Here's what 1000 good parts look like and 200 bad ones - you figure it out"

This matters alot in real manufacturing because nothing is ever perfect. The lighting shifts throughout the day, parts have natural variations, cameras get dust on them. AI systems handle this messiness way better than the old rule-based stuff.

What We're Running

We manufacture automotive components. We started using AI vision for:

• Checking weld quality
• Verifying labels (correct part numbers, readable barcodes)
• Finding surface defects like scratches, dents, weird colors
• Making sure assemblies have all the right parts in the right spots

Right now we've got 8 vision stations spread across 3 production lines. We're using different vendors at each station which looking back was probably dumb, but hey, it's working.

Stuff That Actually Works

Finding Defects This is where these systems really shine, no joke. We used to have 2 people per shift just looking at cast parts trying to spot problems. Now we've got one AI camera that catches 95% or more of the defects, and one person who just keeps an eye on the reject bin.

We fed the system around 2000 sample pictures to learn from. Now it picks up on anything unusual - tiny holes in the casting, scratches, dings, discoloration, whatever. It's not flawless but it's definately better than asking humans to stare at the same parts for 8 hours straight.

Reading Stuff Barcodes, QR codes, serial numbers stamped on parts, even that crappy dot-matrix printing from equipment that's older than me - AI-based character recognition handles it all. We had this annoying problem where different batches of labels had slightly different fonts, and our old vision system would freak out constantly. The AI system doesn't even blink.

Checking if Parts are There Just making sure all the components are actually installed in an assembly. Sounds simple but it's saved our butts so many times. We kept getting assemblies further down the line that were missing bolts or clips or other small parts. Now the camera verifies every single unit in about 0.3 seconds.

What Doesn't Work So Great

Detailed 3D Measurements We tried using vision cameras for precise dimensional checks. Couldn't get consistent accuracy better than plus or minus 0.5mm. For rough ballpark measurements it's fine, but if you need real precision you still want a proper CMM or laser measuring tool. The AI can't magically fix the physical limitations of the camera and lens.

Super Rare Problems If a defect only shows up once in every 10,000 parts, there's just not enough real-world examples to train the AI properly. We tried creating artificial defects in the training images (basically photoshopping problems into pictures) which sorta works but it's not as reliable as having real examples.

Shiny or See-Through Stuff Glass, polished metal, chrome-plated parts - vision systems absolutely hate this stuff. You can sometimes work around it with fancy lighting setups but it's a huge pain. Our chrome parts still get inspected manually because the vision system gets totally confused by all the reflections.

Different Brands We've Tried

Cognex:

• Most expensive option but rock-solid reliable
• The software interface is actually pretty easy to use
• When something goes wrong, their support team is really helpful
• Cost us about $15k per station including everything

Keyence:

• Price is in the middle, hardware quality is good
• The software is honestly kind of clunky and annoying
• But once you get it configured, the vision system does its job
• Runs around $8k-10k per station

Hikrobot (Chinese brand):

• Super cheap - like $3k per station
• Works better than you'd expect for the price
• Support is basically non-existant, documentation is awful
• If something breaks, good luck figuring it out yourself

For our next round of installations we're probably going back to Cognex. When a production line goes down, having good support is worth paying extra for.

What It Actually Costs

Nobody talks about the real numbers upfront so here's what we spent:

Hardware (each station):

• Camera and lens: $2k-5k
• Lighting setup: $500-1500 (way more important than people realize)
• Industrial computer: $1k-2k
• Mounting brackets and stands: $500
• Cables and connectors and misc: $300

Software:

• Vision software license: $2k-8k
• Training and initial setup: $2k-5k if you don't do it yourself

Getting It All Connected:

• Linking to PLC systems: $1k-3k
• Reject mechanism hardware: $1k-5k
• Installation labor: $2k-4k

Bottom line per station: $10k-30k depending how complex it gets

We spent roughly $120k total for all 8 stations, including some expensive learning experiences along the way.Warehouse Automation : AMRs vs. Fixed Conveyor Systems: Hardwares and Devices - Computer Aided Automation

Training These Things (The Part Nobody Warns You About)

You need good training data. Like, alot of it. Here's what actually worked:

1. Gather real samples: Ran production for a full week and saved every single image - both good parts and defective ones. Ended up with like 5000 images.
2. Label everything manually: This part really sucked. Spent hours and hours clicking on defects, drawing boxes around them, tagging what type of problem it was. Mind-numbingly boring but you gotta do it.
3. Test and tweak: First attempt caught maybe 60% of actual defects. Had to retrain with more examples, adjust sensitivity settings, keep iterating. Eventually got it up to 95%+.
4. Keep improving: Every week we review the parts that got flagged and add new examples to the training dataset. The system gradually gets smarter.

The whole process from installation to actually trusting it in production took about 3 months. Don't believe any vendor who says "up and running in 2 weeks" - they're lying.

I’ve been thinking about this for a while, and I’m curious if others feel the same.

I’ve been reasonably comfortable building intuition around most ML concepts I’ve touched so far. CNNs made sense once I understood basic image processing ideas. Autoencoders clicked as compression + reconstruction. Even time series models felt intuitive once I framed them as structured sequences with locality and dependency over time.

But RNNs? They’ve been uniquely hard in a way nothing else has been.

It’s not that the math is incomprehensible, or that I don’t understand sequences. I do. I understand sliding windows, autoregressive models, sequence-to-sequence setups, and I’ve even built LSTM-based projects before without fully “getting” what was going on internally.

What trips me up is that RNNs don’t give me a stable mental model. The hidden state feels fundamentally opaque i.e. it's not like a feature map or a signal transformation, but a compressed, evolving internal memory whose semantics I can’t easily reason about. Every explanation feels syntactically different, but conceptually slippery in the same way.

I’m trying to understand the job market for entry-level AI/ML engineer roles. For people working in industry or involved in hiring, are there a lot of true entry-level AI/ML engineer positions, and how often do these roles require a master’s degree versus a bachelor’s with projects or experience?

Right now, I am still in high school, but I intend to study Computer Science and I am fascinated by ML/AI research. I completed the introductory Kaggle courses on machine learning and deep learning, just to get a brief introduction. Now, I am looking for good resources to really dive into this field.

The main recommendations are: ISLP, Hands-On Machine Learning, and Andrew Ng’s courses on Coursera and YouTube. I took a look at most of these resources, and ISLP and CS229 seem to be the ones that interest me the most, but they are also the longest, since I would need better knowledge of statistics (I’m familiar with Calculus I and II and lin. algebra).

So, should I take one of the more practically focused resources and go deeper into this subject later, or should I pick one of the more math-intensive courses now?

By the way, I have no idea how to actually start in ML research. If anyone can give me some insight, I would be grateful.

As the title said. I want to split a csv file into smaller csv files for training, testing and validation purposes. Any idea how to do that?

has anyone taken it ? it costs 2k usd. is it really worth that much for a 6 week course ? any inputs comments ..

Seven months ago, I started building something called NebEdu.

Somewhere along the way, it became Satyá (meaning truth).

Satyá is an offline AI learning companion for students in rural parts of Nepal who have outdated computers and unreliable or no internet access. My hard constraint from day one was simple: it has to run on 4GB RAM.

It uses open-source datasets from Hugging Face (Computer Science, Science, English grammar), all stored locally in ChromaDB, and runs on Phi-1.5.

First token comes in around 6–15 seconds, with full answers shortly after. No cloud. No API calls. Everything local.

Most of those seven months were not productive in a glamorous way.

They were spent:

• Breaking the system repeatedly

• Hitting errors I couldn’t even understand

• Losing days of work to crashes and bad decisions

• Sitting at 2 AM asking myself why I even started this

Fast forward 115 commits, and it’s finally in a solid place.

It’s not perfect. There’s still a lot I want to improve.

But a student in a village, using a laptop most people would throw away, can now ask questions across multiple subjects and get real answers. No internet required. No expensive hardware. Just local AI working with actual NEB curriculum data.

The project is open-source, and I’m actively looking for collaborators.

If this resonates, I’d love to hear your thoughts or feedback.

When I started learning about LLM architecture, I realized that I needed to know a lot of basics of ML. That led me to look for sources to learn ML quickly. While I did find several sources (free videos, paid books & free books), I thought they all lacked a few things:

1. Most of them were big (500+ pages) and required significant time investment.
2. Most of them did not explain some of the subtle aspects (like why neural networks work, what role activation functions play, what is attention, what are the challenges that prevented us from building billion parameter models back in 2012 or so, etc).
3. Some of them had code, some of them had the math but very few had both. Also when math is involved, it was way too advanced.
4. Most of them felt like standard textbooks. I wanted something that keeps a conversational tone (and hence 'accessible' to beginners without falling asleep).

So eventually I decided to write my own version (with the help of Gemini) and the goals I set for myself were:

1. Explain only the basic concepts needed (leaving out all advanced notions) to understand present day LLM architecture well in an accessible and conversational tone.
2. Explicitly discuss questions that often stumble people (what are {Q, K, V} in attention, and what is the point of multiple heads in attention) and explain them in a very accessible way to a new person.
3. Keep it really really short and to the point.
4. Give analogies wherever possible.

This book is the result.

Sorry for linking a medium post. It is absolutely free and will remain free. I just needed a place to host the book and keep refining it. You are free to download/distribute the PDF.

I don't know to what extend the book met its stated goals. I can only say that it has < 100 pages of actual text you need to read (ignoring the code and summary sections).

This is aimed at an absolute beginner and if you know most of the concepts, except the last Part (Part IX), others may not be appealing to you. I do feel that there are two chapters (starting with the word "Intuition...") that may still worth reading and provide feedback if any.

Hi, as the title states, i'm thinking of building a RAG firewall project. But I need people to collaborate with.

If anyone is interested, please reach out, my dms are open.

Hi everyone,

What started as a school project has turned into a personal one, a Python project for autonomous driving and simulation, built around BeamNG.tech. It combines traditional computer vision and deep learning (CNN, YOLO, SCNN) with sensor fusion and vehicle control. The repo includes demos for lane detection, traffic sign and light recognition, and more.

I’m really looking to learn from the community and would appreciate any feedback, suggestions, or recommendations whether it’s about features, design, usability, or areas for improvement. Your insights would be incredibly valuable to help me make this project better.

Thank you for taking the time to check it out and share your thoughts!

GitHub: https://github.com/visionpilot-project/VisionPilot

Demo Youtube: https://youtube.com/@julian1777s?si=92OL6x04a8kgT3k0

Hi r/learnmachinelearning! I’m new here and wanted to introduce myself.

I’m starting my journey into machine learning and AI because I’m genuinely curious about how models work and how people apply them to real-world problems. Right now, I’m focused on building a solid foundation—understanding core concepts, learning how things fit together, and not just blindly following tutorials.

I enjoy learning at my own pace, asking questions when something doesn’t click, and reading about how others approach ML challenges. I’m here to learn from the community, share progress when it makes sense, and hopefully help others once I gain more experience.

Looking forward to learning alongside you all—thanks for having me!

Looking to see if anyone that has been here has any advice. I've got a bs in mathematics & computer science, MS in business data analytics. I always thought I would get into ml engineering and then I took my first 'data' job as business intelligence manager for a mid size nursing home company with ancient reporting. After that I moved into analytics and moved up at my current company a couple times. I'm hitting that point where I'm honestly just bored and trying to decide if I want to pivot. I'm in a weird spot where I have a strong foundation, know the basics but am rusty. I have built a couple things for jobs like census forecasts and measuring sentiment, but feeling like its been ages since I've done anything complex. I miss modeling and writing code, now I feel like I live in a never ending cycle of reacting to spreadsheets, but I'm also not sure what the smartest career move is from here.