End to End ML Model deployment using AWS Sagemaker

Introduction

This project showcases the end-to-end deployment of a Random Forest multi-class classifier model on AWS SageMaker to predict the price range of mobile phones. The code and dataset are available in the repository.

Project Overview

The project aimed to train and deploy a Random-Forest multi-class classifier model on AWS Sagemaker to predict the price range of mobile phones. I followed Krish Naik’s tutorial “End-to-end Machine Learning Project Implementation Using AWS Sagemaker”. Duration of project: 3 hours.

Tools

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 model training.
• requirements.txt: File listing the required packages for the project.
• mob_price_classification_train.csv: Dataset used for training the model.
• train-V-1.csv and test-V-1.csv: Train and test data files.

Installation

To install the necessary packages, run the following command:

pip install -r requirements.txt

Conclusion

This project demonstrates the deployment of a machine learning model on AWS SageMaker, showcasing the importance of deploying models for real-world applications. For a detailed walkthrough, refer to the notebook provided.

Medium article: End-to-end ML model deployment using AWS Sagemaker: Project review