Which AWS service or feature can help an AI development team quickly deploy and consume a foundation model (FM) within the team's VPC?

Amazon SageMaker JumpStart is a machine learning hub that accelerates the ML journey by providing access to hundreds of pre-trained models, including state-of-the-art foundation models (FMs). It allows development teams to select an FM and deploy it with a single click, which automatically creates a secure, scalable Amazon SageMaker endpoint. This endpoint can be configured to be accessible only from within the team's Virtual Private Cloud (VPC), satisfying the requirement for quick deployment and private consumption.

1. Amazon SageMaker Developer Guide: "Amazon SageMaker JumpStart helps you quickly and easily get started with machine learning. JumpStart provides one-click deployment and fine-tuning of a wide variety of pre-trained models from popular model hubs

including foundation models."

Source: AWS Documentation

Amazon SageMaker Developer Guide

"Amazon SageMaker JumpStart".

2. Amazon SageMaker Developer Guide: "To control access to your models

we recommend that you configure your SageMaker to use a private VPC... When you use a private VPC

you can configure it so that your model containers aren't accessible over the internet."

Source: AWS Documentation

Amazon SageMaker Developer Guide

"Protect Endpoints by Using a Virtual Private Cloud".

3. AWS Machine Learning Blog: "With SageMaker JumpStart

you can choose from a growing list of best-performing foundation models... With one-click deployment

you can get a dedicated endpoint for your chosen model and use it in your applications."

Source: AWS Machine Learning Blog

"Amazon SageMaker JumpStart simplifies and accelerates access to foundation models for generative AI"

May 25

2023.