In this talk, we'll bridge the gap between data science and web development by demonstrating how to first train and then deploy a machine learning model using Django. Starting with the classic Iris dataset, we'll walk through training a classification model with scikit-learn, covering key steps like data preprocessing, model selection, and evaluation.

Once the model is trained, we'll shift focus to Django, showcasing how to integrate the model into a web application that allows users to input flower measurements and receive real-time predictions of the Iris species.

You'll learn how to take a data science project from a Jupyter notebook to a fully functional Django web app in just a few steps. Whether you're a data scientist interested in web deployment or a Django developer exploring machine learning, this talk will equip you with the tools to bring machine learning models into production-ready applications.