Deep knowledge

Hey friends! Big news! 🌟
I’ve started working on something super exciting — a Machine Learning book 📘🤖
The idea is to make ML #easy, #fun, and #practical with:
✅ Simple explanations
✅ Python code examples
✅ Step-by-step exercises
✅ Real-world applications
But here’s the best part 👉 I don’t want to do this alone!
I’d love to grow together as a community while building this.
If you’re into ML/AI/Data Science and want to:
✨ Share ideas
✨ Contribute code or examples
✨ Give feedback on chapters
✨ just learn while collaborating
✨ Or even collaborate as a co-author
… then you’re more than welcome to join in! 💡
🔗 Check out the project here:
[GitHub Repository]
(github.com/DeepKnowledge1/ml)

Drop a comment if you’re interested, and let’s make this a journey where we learn, teach, and create something valuable together

3 weeks ago | [YT] | 5

Deep knowledge

I’ve been obsessing over edge-ready anomaly detection—tiny footprint, big accuracy, no GPU. So I pitted my lean PaDiM pipeline against Anomalib on MVTec (bottle), CPU-only. The results made me smile 😎



Checkout: github.com/DeepKnowledge1/AnomaVision







✨ Headline Results
⚡ Latency: 22.5 ms/image (44.5 FPS) vs 105.3 ms (9.5 FPS)
🎯 Image AUROC: 0.9968 (mine) vs 0.9960
🧩 Pixel AUROC: 0.9837 (mine) vs 0.9869
💾 Model+stats size: 15.25 MB vs 40.47 MB





TL;DR: ~4–5× faster on CPU with matching image-level accuracy and a much smaller footprint. Anomalib edges me on pixel AUROC by a whisker (I’m coming for it 😉).




#AnomalyDetection #ComputerVision #EdgeAI #MLOps #PyTorch #Anomalib #PaDiM #ONNX #TensorRT #AIEngineering #ModelOptimization #DeployOnCPU #GPU

1 month ago | [YT] | 3

Deep knowledge

🚧 AnomaVision Update Incoming 🚧

Hey everyone 👋 — I’m currently updating and refactoring the AnomaVision repository, and as part of that, the industrial_anodet_mlops component might not be working properly with the new changes at the moment.

Please bear with me — I’ll be working on fixing everything soon and making sure all modules are fully compatible again. Thanks for your patience and support! 🙏
Stay tuned for more updates.

2 months ago | [YT] | 5

Deep knowledge

🚀 AnomaVision – Next-Gen Visual Anomaly Detection

Production-ready. Lightning fast. Edge-to-Cloud deployment.
Powered by PaDiM and built for real-world applications.

✅ 40–60% less memory
✅ ONNX & PT support
✅ Enterprise-grade visualizations

👉 Clone now and start detecting anomalies in 2 minutes.
🔗 github.com/DeepKnowledge1/AnomaVision

💡 Interested in contributing? We’re actively looking for collaborators — code, docs, tests, features… every contribution is welcome!

2 months ago | [YT] | 2

Deep knowledge

✅ We Did It!
I’ve just wrapped up the full Industrial MLOps Project – thank you all for the amazing support and engagement! 🙌

🎯 What’s Next?
Starting next Monday or Tuesday, I’ll be continuing with Python for Computer Vision – packed with hands-on tutorials, real-world applications, and beginner-friendly explanations. 🔍🧠💻

If you're excited to explore the world of image processing, object detection, and more using Python, stay tuned!
💬 Drop a comment if there's a specific topic or project you want to see.

#MLOps #ComputerVision #Python #AI #DeepLearning #MachineLearning #YouTubeLearning

2 months ago | [YT] | 6

Deep knowledge

🧠 Industrial MLOps Stack Setup – Part 2 is coming your way!
We’re diving even deeper into the world of MLOps with pro-level setups, automation tips, and real-world use cases 💻⚙️

Whether you're just starting out or already deploying models, this next part is packed with 🔥 value.

📅 Set your reminders
📌 Subscribe & turn on notifications
👀 You don't want to miss this one!

#mlops #devops #machinelearning #comingsoon #mlengineering #azureml #docker #python

4 months ago | [YT] | 2

Deep knowledge

🚀 BIG ANNOUNCEMENT 🚀

After intensive preparation, research, and real-world system design,
I’m officially launching a FREE YouTube series on:

🎯 Industrial MLOps with Azure ML & AKS — Full Production Pipeline 🎯



✅ No toy projects.


✅ No "just Jupyter notebooks."


✅ 100% real-world enterprise ML system design.


✅ End-to-end pipelines, monitoring, deployment, security, and automation — exactly like real companies build.





📅 First Episode:
📌 Thursday, June 11, 2025





✅ This series will cover:



📦 Azure ML Pipelines

🔎 PaDiM Industrial Defect Detection (Real Use Case)

☸️ AKS Deployment (Kubernetes)

⚡ FastAPI Inference

📊 MLflow Tracking

🔄 Full CI/CD with Azure DevOps

📈 Application Insights Monitoring

🌊 Drift Detection

📉 Load Testing

🔐 Key Vault Security

🐳 Full Dockerization






If you're serious about learning how real companies build ML systems, this series is for you.

🎯 Subscribe, turn on notifications 🔔, and get ready.

🔥 Let’s build real industrial ML pipelines — not toy models.




#MLOps #AzureML #AKS #IndustrialAI #MachineLearning #AzureDevOps #FullStackML #MLOpsPipeline #PaDiM #AnomalyDetection #ProductionML

4 months ago | [YT] | 4

Deep knowledge

🛠 IMPORTANT! Your Opinion Needed:

I’m preparing the FULL FREE Azure ML MLOps Industrial Course 🎯

✅ Real-world project:
Anomaly Detection with PaDiM (Industrial Defect Detection)
✅ Azure ML, AKS, FastAPI, Monitoring, Drift Detection, CI/CD

BUT 👉 Azure Free Tier doesn’t cover everything 💰
👉 Cost depends on how carefully you use cloud resources: shut down clusters, optimize compute, avoid unnecessary runs, etc.

⚠ Estimated cost if fully executed: ~*$30- $50*(can be less if you're careful).

👉 Are you OK with this topic & minimal Azure cost for full hands-on industrial MLOps?

4 months ago | [YT] | 4

Deep knowledge

I’m super excited to bring you more deep-dive content — but I want YOUR input 👇

4 months ago | [YT] | 8

Deep knowledge

🎉 Python OOP Explained with Stunning Neon 3D Visuals! 🎉

Hey everyone! In today’s video, I’m breaking down the core concepts of Object-Oriented Programming (OOP) in Python using a brand new, super catchy neon 3D infographic. Whether you’re a beginner or just want a quick refresher, you’re in the right place!

Here’s what we’ll cover—with a simple definition for each:

👉 Attributes & Objects:
Objects are things you create in your code, and attributes are their details—just like a car object with a color attribute.

👉 Method Kinds:
Different types of functions inside a class: instance methods, static methods, and class methods—each with its own role.

👉 Encapsulation:
Keep your code safe by hiding the complicated stuff and only showing what matters—like a remote control for your TV.

👉 Polymorphism:
One action, many results! Different objects can react differently to the same method.

👉 Abstraction:
Show only what’s needed and hide the rest. It’s like using a car without worrying about the engine.

👉 Abstract Classes:
Think of these as blueprints for other classes—laying out what features they need, but you can’t use them directly.

🔥 Visuals make all the difference, so make sure to check out the infographic in the video! If you find OOP confusing, this will help you finally “see” how it all fits together.

If you enjoyed this video, don’t forget to like, subscribe, and let me know in the comments which OOP principle you found hardest or most interesting!

#Python #OOP #Programming #LearnPython #Coding #Education

4 months ago | [YT] | 4