BUILD YOUR MACHINE LEARNING Web-App IN MINUTES - Part 4
🚀 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...
Part 4: Before diving into the world of Amazon SageMaker, it's essential to first build and evaluate machine learning models on your local computer. Using tools like Jupyter Notebook, you can create a conducive environment for model development and experimentation. By leveraging libraries such as scikit-learn, TensorFlow, or PyTorch, you can train models using your local dataset and fine-tune them to achieve optimal performance.
This local development phase allows you to iterate quickly, test different algorithms, and explore various hyperparameters without incurring costs associated with cloud-based resources. Additionally, it provides an opportunity to familiarize yourself with the intricacies of your dataset, understand feature engineering requirements, and visualize data insights before scaling up to larger datasets on cloud platforms like Amazon SageMaker.
Overall, starting with local model development in Jupyter Notebook lays a solid foundation for your machine learning journey, enabling you to gain valuable experience and refine your models before transitioning to more scalable and collaborative environments like SageMaker.
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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