rafaelgreca/e2e-mlops-project
The purpose of this project's design, development, and structure is to create an end-to-end Machine Learning Operations (MLOps) lifecycle to classify an individual's level of obesity based on their physical characteristics and eating habits.
This project helps data scientists transition their machine learning experiments from development notebooks into robust, deployable systems. It takes raw data and exploratory analysis from Jupyter notebooks and generates a production-ready system that can classify an individual's obesity level based on their physical characteristics and eating habits. Data scientists will use this to streamline their workflow from research to deployment.
No commits in the last 6 months.
Use this if you are a data scientist looking for a structured example of how to convert your experimental machine learning code into a robust, deployable, end-to-end MLOps solution.
Not ideal if you are looking for the absolute best machine learning model for obesity classification, as its primary purpose is demonstrating MLOps principles, not achieving state-of-the-art model performance.
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MIT
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Last pushed
Nov 30, 2024
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