Model Deployment

Project description

Methods used

• Data preparation
• Model training
• Model evaluation
• Model deployment

Technologies

• Data Preparation: funpymodeling and pandas.
• Model Training: sklearn.
• Deployment: FastAPI, requests, mlflow and json.
• Others: numpy and pickles.

Installation

1. Clone the repository:
   git clone https://github.com/ITRoselloSignoris/Data-Science-Model-Deployment-MLflow

2. Install the necessary libraries inside the requirements.txt:
   pip install -r requirements.txt

To-Do list

• Write project description