Deep knowledge

Hey everyone! 👋
I’m planning the next big series for the channel and need your help! 😄

💥 What do you want to learn next?


Drop your vote in the comments ⬇️
Your choice decides what I’ll create next! 💪

#PyTorch #CICD #MachineLearning #DevOps #CodingCommunity

1 month ago | [YT] | 5

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

2 months 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

3 months ago | [YT] | 4

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.

3 months ago | [YT] | 6

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!

3 months ago | [YT] | 3

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

4 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

5 months ago | [YT] | 3

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

6 months ago | [YT] | 5

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?

6 months ago | [YT] | 5

Deep knowledge

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

6 months ago | [YT] | 9