This article details the process of deploying a trained machine learning model as a web application using Flask. It covers the workflow from model development to making it accessible to end-users through a web interface. The focus is on practical MLOps techniques to bridge the gap between model creation and real-world application. AI
IMPACT Provides practical guidance for developers on integrating ML models into web applications.
RANK_REASON Article describes a technical workflow for deploying an existing ML model, not a new release or significant industry event.
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