Anomavision From Code to PyPI 📦 Build, Publish & Run Anomaly Detection

In this video, I’m excited to showcase my own Python package — AnomaVision 🎉
You’ll see exactly how I built it, published it to PyPI, installed it with pip install anomavision, and then used it in a real anomaly detection demo.

This session is perfect for:

🐍 Beginners → Learn how to publish your first Python package step by step.

💻 Pros → See how to structure a package, automate publishing, and run advanced inference with AnomaVision.

🔎 What we’ll cover in this video:

📂 Package structure & setup for PyPI

🏗️ Building the package (wheel & sdist)

🚀 Publishing to PyPI with twine

📥 Installing via pip install anomavision

🤖 Running AnomaVision inference on test images

🎨 Visualizing results with heatmaps, highlighted anomalies & boundaries

At the end, you’ll see how easy it is for anyone to install and use AnomaVision — whether you’re just starting out or already a pro.

👉 Try it yourself:

pip install anomavision


💡 If you enjoy the tutorial and demo, don’t forget to like 👍, subscribe 🔔, and share to support more Python developers and ML enthusiasts!

🔖 Hashtags

#anomavision #python #pypi #anomalydetection #machinelearning #deeplearning #pipinstall #pythonpackage #opensource #pythonforbeginners #mlops #datascience