🚀 Welcome to the Sentiment Analysis Project! This project focuses on building a sentiment analysis model for analyzing sentiment in text data. The model is designed to automate the process of analyzing sentiment in text, providing valuable insights into the emotional tone of the text.
🤖 Automated Model Building: The sentiment analysis model is built using automated pipelines, ensuring consistency and reproducibility in the model training process.
MLOps practices are implemented to manage the machine learning lifecycle effectively. Continuous integration and delivery pipelines are set up to automate model deployment and monitoring.
Model experiments are continuously tracked using MLflow, allowing easy comparison of different model versions and configurations.
The model is continuously trained every one minute to adapt to changing data patterns and improve performance over time.
A user interface is developed using Streamlit for easy interaction with the sentiment analysis model.
Automated pipelines are used to streamline the model building process, ensuring efficient and consistent development.
MLOps practices are implemented to automate and streamline the machine learning lifecycle, from development to deployment and monitoring.
MLflow is used for experiment tracking and management, providing insights into model performance and facilitating model iteration and improvement.
CI/CD pipelines are set up to automate model deployment and monitoring, ensuring seamless integration of new features and updates.
🔍 To get started with this project, follow these steps:
Clone the repository to your local machine using git clone.
Install project dependencies listed in requirements.txt using pip install -r requirements.txt.
Explore the Jupyter notebooks in the notebooks/ directory for data exploration and run the orchestration.py file for continues model training .
Run the Streamlit user interface for easy interaction with the sentiment analysis model.
Access the deployed model API at http://localhost:5000 and start making predictions.
🤝 Contributions to this project are welcome! If you have any ideas, suggestions, or improvements, feel free to open an issue or submit a pull request.
📜 This project is licensed under the MIT License - see the LICENSE file for details.
app: https://sentimentanalysisbyanand.streamlit.app/