https://github.com/itsshreyagarg/sentify-ml

An end-to-end sentiment analysis API powered by machine learning! 🚀 This project features text classification, FastAPI deployment, Docker containerization, CI/CD automation, and real-time monitoring with MLflow & Grafana. Built for seamless cloud deployment on Azure. Let me know if you want any tweaks!
https://github.com/itsshreyagarg/sentify-ml

fastapi jupyter-notebook mlflow

Last synced: about 2 months ago
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An end-to-end sentiment analysis API powered by machine learning! 🚀 This project features text classification, FastAPI deployment, Docker containerization, CI/CD automation, and real-time monitoring with MLflow & Grafana. Built for seamless cloud deployment on Azure. Let me know if you want any tweaks!

• Host: GitHub
• URL: https://github.com/itsshreyagarg/sentify-ml
• Owner: Itsshreyagarg
• Created: 2025-03-13T13:01:16.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-03-13T14:06:49.000Z (over 1 year ago)
• Last Synced: 2025-03-13T14:24:31.454Z (over 1 year ago)
• Topics: fastapi, jupyter-notebook, mlflow
• Language: Jupyter Notebook
• Homepage:
• Size: 94.1 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Sentify-ML

An end-to-end sentiment analysis API powered by machine learning! This project features text classification, FastAPI deployment, Docker containerization, CI/CD automation, and real-time monitoring with MLflow & Grafana. Built for seamless cloud deployment on Azure.  Let me know if you want any tweaks! 

## Table of Contents

- [Features](#features)

- [Tech Stack](#techstack)

- [Usage](#usage)

- [Overview](#overview)

- [Endpoints](#endpoints)

- [Contact](#contact)

  

## Features

- Text Preprocessing: Stopword removal, tokenization, TF-IDF/Word Embeddings.

- ML Model: Trained using Logistic Regression, Naive Bayes, LSTM, or BERT.

- FastAPI Backend: Exposes a /predict endpoint for sentiment classification.

- Containerization: Dockerized for scalable deployment.

- CI/CD Pipeline: Automated deployment using GitHub Actions/Jenkins.

- Cloud Deployment: Hosted on GCP (Google Cloud Run/Compute Engine).

- Monitoring & Logging: Logs API requests with Loguru; tracks model performance with MLflow.

## Tech Stack

- Backend: FastAPI

- Machine Learning: Scikit-learn, TensorFlow/Keras, Hugging Face (for BERT)

- Containerization: Docker

- Cloud Deployment: GCP (Cloud Run / Compute Engine)

- CI/CD: GitHub Actions, Jenkins

- Logging & Monitoring: Loguru, MLflow

## Contact

For any questions or inquiries, please contact the project maintainer:

- Name: Shreya Garg

- Email: shreyagarg754@gmail.com

- GitHub: Itsshreyagarg