Deploying a Machine Learning Model with AWS SageMaker

Introduction

This project exemplifies deploying a multi-class classifier model using Random Forest on AWS SageMaker to predict mobile phone price ranges. The repository contains the necessary code and dataset.

Project Overview

The goal of this project was to develop and deploy a Random Forest multi-class classifier model on AWS SageMaker for predicting mobile phone price ranges.

Tools Utilized

• VS Code
• Anaconda
• AWS SageMaker
• AWS S3
• AWS IAM

Project Structure

• sagemaker-custom-script.ipynb: Jupyter Notebook containing the project implementation.
• script.py: Python script used for training the model.
• requirements.txt: File listing required packages for the project.
• mob_price_classification_train.csv: Dataset used for model training.
• train-V-1.csv and test-V-1.csv: Training and testing data files.

Installation

To install necessary packages, execute the following command:

pip install -r requirements.txt

Conclusion

This project illustrates the deployment of a machine learning model on AWS SageMaker, underscoring the significance of deploying models in real-world scenarios. For a detailed guide, refer to the provided notebook.

References:

• Video Tutorial: “Implementing an End-to-end Machine Learning Project Using AWS SageMaker” (Duration: 3 hours)