BUILD YOUR MACHINE LEARNING Web-App IN MINUTES - Part 10
🚀 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 10: In this part, we're taking a closer look at the endpoints generated after implementing our XGBoost model. Join us as we explore how these endpoints serve as gateways for our machine learning model, allowing us to interact with it seamlessly. We'll walk through the process of accessing these endpoints, sending data for prediction, and receiving responses from our model in real-time. Get ready to witness the power of deployment as we bring our machine learning model to life through these endpoints.
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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