AI Image Classifier.

Tech Stack

• Python
• TensorFlow
• Flask

Problem

Many machine learning projects stop at model training and never address deployment or usability. The goal was to build a complete pipeline—from training to a usable web interface.

Solution

I trained an image classification model using TensorFlow and deployed it through a Flask application. Users can upload images and receive predictions in real time, making the model accessible beyond a notebook environment.

Key Decisions

• —Focused on end-to-end workflow rather than model accuracy alone.
• —Used Flask to keep deployment lightweight and understandable.
• —Prioritized inference speed and stability over complex architectures.

Outcome

The project resulted in a fully functional ML application that demonstrates practical deployment skills and an understanding of real-world ML constraints.