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 week ago | [YT] | 2

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.

1 month ago | [YT] | 4

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!

1 month ago | [YT] | 1

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

1 month ago | [YT] | 5

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

3 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

3 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?

3 months ago | [YT] | 4

Deep knowledge

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

3 months ago | [YT] | 7

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

3 months ago | [YT] | 3

Deep knowledge

7. Lasso vs Ridge Understanding L1& L2 Regularization in Linear Regression with Numerical Example &🐍



Ready to master regularization in linear regression? In this step-by-step tutorial, we dive deep into the world of L1 (Lasso) and L2 (Ridge) regularization—two powerful techniques to prevent overfitting and improve model performance. But here's the twist: we implement everything from scratch using pure Python, without relying on external libraries! 💻✨

This video is perfect for both beginners and advanced learners who want to understand the mathematical foundations of regularization and see how it works under the hood. By the end, you'll know how to:
✅ Build a linear regression model with L1 and L2 regularization
✅ Compute gradients and update coefficients manually
✅ Compare Lasso and Ridge regularization and choose the right one for your problem
✅ Implement these techniques entirely in pure Python

No black-box machine learning libraries here—just raw code and clear explanations. Whether you're new to machine learning or looking to deepen your understanding, this tutorial has something for everyone. Let's build smarter models together! 🚀

🔗 Watch Now: [Insert Video Link Here]
📌 Key Topics Covered:
- Linear Regression Basics 🧮
- Overfitting and Regularization 🔍
- L1 (Lasso) vs L2 (Ridge) Regularization ⚖️
- Pure Python Implementation 💻✨
- Numerical Example Walkthrough 🧩

Don't forget to like, share, and subscribe for more tutorials on machine learning, data science, and beyond! Let’s continue learning together. 👩‍💻👨‍💻

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htags:
#MachineLearning #DataScience #LinearRegression #Regularization #Lasso #Ridge #PythonTutorial #AI #DeepLearning #FromScratch #PurePython #BeginnersGuide #AdvancedML

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💬 Call-to-Action:
Have questions about regularization or need help implementing it? Drop a comment below, and I’ll be happy to assist! If you enjoyed this video, consider subscribing for more in-depth tutorials on machine learning, statistics, and data science. Let’s keep building smarter models together! 🌟

6 months ago | [YT] | 3