BUILD YOUR MACHINE LEARNING Web-App IN MINUTES - Part 6
Welcome to our series! We're going to build a machine learning web app from scratch. Whether you're a pro or a beginner, this series is for you. We'll use tools like Amazon SageMaker, AWS Amplify, and more to make our app shine. Our goal? To teach you the basics of ML focused AWS tools integration and web development, one step at a time. Ready to dive in? Let's get started! Here are few key links:
https://aws.amazon.com
https://www.anaconda.com/download
https://www.kaggle.com/datasets/bsugi...
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Part 6: In this segment, we'll walk you through the process of setting up an Amazon SageMaker notebook instance, a key component for developing, training, and deploying machine learning models on AWS. Using the AWS Management Console, we'll guide you through the steps to create a new notebook instance, selecting the instance type, and configuring security settings. With your SageMaker notebook instance up and running, you'll have a powerful cloud-based environment equipped with Jupyter notebooks, ready to explore datasets, experiment with machine learning algorithms, and collaborate with team members seamlessly.
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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