BUILD YOUR MACHINE LEARNING Web-App IN MINUTES - Part 11
🚀 Welcome to our epic series! Get ready to dive into building a machine learning web app, no matter your skill level! We'll harness the power of Amazon SageMaker, AWS Amplify, and more to make our app shine bright! Our mission? To guide you through the essentials of ML-focused AWS tools integration and web development, step by step. Ready to start your journey? Let's go! 🔥 Here are few key links:
https://aws.amazon.com
https://www.anaconda.com/download
https://www.kaggle.com/datasets/bsugi...
Support us: https://paypal.com/donate/?hosted_but...
Music credit: Free Piano Music For YouTube - "Purpose" by Jonny Easton
Part 11: In this segment, we're taking a step back to look at the big picture. Join us as we unveil the architecture diagram/schema that guides our journey in this series to create a powerful web application. From sourcing data on Kaggle to meticulous data cleaning and feature engineering, we'll showcase each step in our process. Then, we'll delve into the heart of our application, utilizing SageMaker for model development, Lambda functions for serverless computing, API Gateway for seamless integration, and AWS Amplify for full realization.
To follow along, you’ll need:
*A text editor (such as Notepad or Notepad++)
*An AWS account and access to the Console. Note: You'll need administrator permissions.
*Some basic knowledge of AWS is preferable, but you can still follow along if you’re an absolute newbie.
*Additionally, Microsoft Excel and access to an Anaconda environment for python editor are required.
Below is the complete code and raw data, necessary for constructing the application:
Loan Data file:
https://drive.google.com/drive/folder...
Python program for model evaluation in local machine:
https://drive.google.com/drive/folder...
SageMaker notebook:
https://drive.google.com/drive/folder...
Lambda function file including JSON text file:
https://drive.google.com/drive/folder...
Amplify HTML file:
https://drive.google.com/drive/folder...
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