Computer vision has the power to address critical local and global issues, from agricultural disease detection to medical imaging. However, for many traditional software engineers, building performant models is bottlenecked by intimidating APIs and a perceived need for deep mathematical expertise. This 2-hour tutorial changes that narrative. Designed specifically for Python developers who are entirely new to machine learning, we will use the elegant, highly "Pythonic" FastAI library to build a powerful image classifier from the ground up. We don't just stop at a Jupyter Notebook. This session is built around end-to-end engineering, taking attendees all the way to a deployable model. Session Outline: Introduction & The 10-Line Promise (20 mins): Demystifying the modern open-source AI stack (PyTorch, timm, and FastAI). The Pythonic Data Pipeline (25 mins): Mastering the DataBlock API to load custom image datasets. We'll explore its functional, composable architecture. Training & Debugging (30 mins): We will train a modern baseline model (ConvNeXt). Crucially, we will learn how to debug model failures using learn.plot_top_losses() and see how FastAI seamlessly bridges with standard Pandas DataFrames to analyze errors. The Grand Finale: Web Deployment (35 mins): The killer feature of the tutorial. We will export our trained model (model.pkl) and write an additional 20 lines of pure Python to wrap it into a live, interactive web dashboard using Streamlit. Q&A and Live Troubleshooting (10 mins) Prerequisites: Target Audience: Python developers who are entirely new to deep learning. No calculus, linear algebra, or prior machine learning experience is required. Basic familiarity with Python programming is all you need.