Unlocking Languages with AI: A Journey from Model to Deployment using FastAPI, Docker, and Heroku

Deploying an AI-Powered Language Detection Model

Overview

This project aimed to deploy a Language Detection Machine Learning model that identifies the language of a given text snippet. Utilizing FastAPI for creating a responsive web application, Docker for containerization, and Heroku for deployment, the project showcases a full-stack approach to deploying AI models in production.

Tools and Technologies

• Programming Languages: Python

• Libraries and Frameworks: FastAPI, scikit-learn

• Technologies: Docker, Heroku

• Tools: Jupyter Notebook
  

Approach and Methodology

The project commenced with the development of a language detection model in a Jupyter Notebook using sklearn's CountVectorizer and naive_bayes classifier. A pipeline was constructed to streamline the model's usage. Subsequently, a FastAPI application was developed to serve the model via RESTful endpoints. The application was containerized using Docker, facilitating consistent deployment across different environments. The deployment process was finalized on Heroku, offering a cloud-based platform for accessing the model globally.

Results and Impact

The deployed model successfully predicts the language of input sentences with high accuracy. The project's culmination was demonstrated through a FastAPI documentation page, where users can interact with the model in real-time. This project exemplifies the power of combining machine learning, web development, and cloud technologies to bring AI models into practical use.

Visuals

Code and Repository Link : Deployment_LanguageModel_FastAPI-Docker-Heroku.

Skills

• Technical Skills: Machine Learning, Natural Language Processing, API Development, Docker Containerization, Cloud Deployment (Heroku), Python Programming, Data Visualization