Starters Guide: End-to-End Guide to Building with LLMs on SageMaker

1

AWS Secrets Manager

Credential Management: Avoid storing sensitive data like access keys directly, utilizing AWS Secrets Manager, or using environment variables.

#Identity And Access Management #Identity Provider #SSO 76 social mentions

2

Amazon EC2

Note: When you launch the SageMaker Studio instance, it essentially spins up an EC2 instance behind the scene to run the Jupyter notebook environment. SageMaker manages the EC2 instance for you and handles provisions and scaling of the underlying infrastructure based on your project needs. It is also best practices to terminate (delete) an EC2 instance once the project completion and the instance is no longer needed to avoid unnecessary charges and ensure proper resource management.

#Cloud Computing #Cloud Infrastructure #VPS 73 social mentions

3

AWS Management Console

Step 1: Open your web browser and navigate to the AWS Management Console at https://aws.amazon.com/console/.

#IT Management #Project Management #Task Management 58 social mentions

4

Amazon Bedrock

Next, we need to grant access to Amazon Bedrock—a service that provides fully managed generative AI foundational models. We will be utilizing these connections for our LLM use case. How to grant access includes.

#Utilities #Communications #Large Language Model Tools 41 social mentions

5

AWS Cost Explorer

Cost Management: Set billing alerts and monitor usage with tools like AWS Cost Explorer.

#Monitoring Tools #Log Management #Auditing And Compliance 22 social mentions