Overview
This Guidance helps organizations provide their data scientists with external package repository access while maintaining information security (infosec) compliance. Data scientists must commonly install open-source packages residing in public repositories, but this introduces security risks. By using an automated orchestration pipeline on AWS, organizations can make sure that all public packages undergo comprehensive security scans before entering data scientists’ private Jupyter notebook environments. InfoSec governance controls are seamlessly integrated, providing a smooth data science workflow experience without disruptions. With this Guidance, organizations can strike a balance between empowering data scientists with agility and maintaining robust security measures for operational harmony.
How it works
These technical details feature an architecture diagram to illustrate how to effectively use this solution. The architecture diagram shows the key components and their interactions, providing an overview of the architecture's structure and functionality step-by-step.
Well-Architected Pillars
The architecture diagram above is an example of a Solution created with Well-Architected best practices in mind. To be fully Well-Architected, you should follow as many Well-Architected best practices as possible.
Implementation resources
The sample code is a starting point. It is industry validated, prescriptive but not definitive, and a peek under the hood to help you begin.
Open sample code on GitHub
Disclaimer
The sample code; software libraries; command line tools; proofs of concept; templates; or other related technology (including any of the foregoing that are provided by our personnel) is provided to you as AWS Content under the AWS Customer Agreement, or the relevant written agreement between you and AWS (whichever applies). You should not use this AWS Content in your production accounts, or on production or other critical data. You are responsible for testing, securing, and optimizing the AWS Content, such as sample code, as appropriate for production grade use based on your specific quality control practices and standards. Deploying AWS Content may incur AWS charges for creating or using AWS chargeable resources, such as running Amazon EC2 instances or using Amazon S3 storage.
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