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Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, and modular.
Leverage Metaflow, PyTorch, AWS S3, Elasticsearch, FastAPI and Docker to create a production-ready facial recognition solution. It demonstrates the practical use of deep metric learning to recognize previously unseen faces without prior training.
This project aims to apply MLOps techniques to deploy a machine learning model through an API constructed with FastAPI. We utilize Poetry for dependency management and Docker for containerization, ensuring the code is modular, organized 📐, and maintainable 🛠️.
The project comprises a real-time tweets data pipeline, a sentimental analysis of the tweets module, and a Slack bot to post the tweets' sentiments. The project uses SentimentIntensityAnalyzer from the VaderSentiment library. The analyzer gives positive, negative, and compound scores for small texts (such as tweets in this case). The real-time d…