- Lab
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Libraries: If you want this lab, consider one of these libraries.
- Cloud
Secure ML Data Access with Private S3 Connectivity
In this hands-on lab, you will design and secure access to a machine learning dataset stored in Amazon S3. You will begin by creating an S3 bucket and enabling public access to simulate an insecure baseline. Next, you will transition to a secure architecture by creating a VPC endpoint, associate route table, and apply least-privilege IAM policies to restrict access. Finally, you will validate that the dataset can be accessed only through a private network path from an EC2-based ML workload, while public access is revoked. This lab demonstrates how to implement secure, private, and compliant data access patterns for real-world ML workflows in AWS.
Lab Info
Table of Contents
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Challenge
Create Storage and Components with Public Access
- Create a S3 bucket.
- Make its content public by setting bucket access policy
- Upload ML dataset to the bucket
- Validate public access to the uploaded file using URL
- Author Notes: Ensure learners disable “Block Public Access” before applying the public policy.
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Challenge
Configure the Storage and Network Connectivity Components for Private Access
- Create VPC Gateway Endpoint for S3
- Verify Route Table Configuration
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Challenge
Create ML Workload (EC2 Instance)
- Launch EC2 Instance
- Attach IAM Role to EC2 instance
- Test S3 Access using EC2
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Challenge
Test the Storage and Network Connectivity Components
- Remove Public Access from S3
- Restrict S3 Access to VPC Endpoint Only
- Validate Secure Private Access using EC2
- Confirm public access is revoked using URL
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