Project: An end-to-end NLP binary sentiment classification + CI/CD/CT

✨ Project information:

This project is an LSTM-based text classification system that utilizes the IMDB dataset, which consists of 50K movie reviews for natural language processing. The dataset is suitable for binary sentiment classification and contains substantially more data than previous benchmark datasets, with 25,000 reviews provided for training and 25,000 for testing. The project's primary goal is to predict the number of positive and negative reviews using either classification or deep learning algorithms. Additionally, the project uses Flask and Gunicorn as an endpoint and has been developed using microservice architecture, making it an end-to-end project.

📚 Libraries used :

• Tensorflow
• Keras
• Pandas
• Numpy
• DVC

🔥 Technologies Used:

1. Python 
2. shell scripting 
3. aws cloud Provider 

🔌 Infrastructure:

1. AWS S3
2. GitHub
3. Flask, gunicorn

👷 Initial Setup:

conda create --prefix ./env python=3.9
conda activate ./env 
pip install -r requirements.txt
dvc init

Conclusion

This project is production ready to be used for the similar use cases and it will provide the automated and orchesrated production ready pipelines(Training & Serving)

Thanks for taking a look at this project. If you find it valuable, kindly rate it by clicking the star icon. Your support is highly appreciated! 😊🙏⭐

📃 License

MIT license © My Website website
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