AWS System Manager Parameter Store caching client for Python

🚀 Envilder is a CLI that securely centralizes your environment variables from AWS SSM as a single source of truth

A cross platform desktop application that provides an UI to easily view and manage AWS SSM parameters.

Terraform module to provision a KMS key with alias

Wraps the aws-sdk-go and hides the complexity of dealing with the not so Go friendly AWS SDK.

A human-friendly way of managing parameters in AWS SSM

Simple key-value configuration service

Environment variable-based AWS Parameter Store command shim

Simple utility to help export parameters from Amazon EC2 Systems Manager Parameter Store as environment variables

A simple Python library for getting values from AWS Systems Manager Parameter Store

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Replace Pydantic's builtin Secret Support with a configuration provider that loads parameters from AWS Systems Manager Parameter Store.

Parameter cache for AWS System Manager Parameter Store and AWS Secrets Manager

Terraform module to provision an IAM role with configurable permissions to access SSM Parameter Store

With Amazon Rekognition Custom Labels, you can easily build and deploy Machine Learning (ML) models to identify custom objects which are specific to your business domain in images without requiring advanced ML knowledge. When combined with Amazon Augmented AI (A2I), you can quickly integrate a ML workflow to capture and label images with a human workforce for model training. As ML lifecycle is an iterative and repetitive process, you need to implement an effective workflow that can provide for continuous model training with new data and automated deployment. Your workflow also needs to be flexible enough to allow for changes without requiring development rework as your business objectives change. Operationalizing an effective and flexible workflow can be resource intensive, especially for customers who have limited machine learning capabilities. In this post, we will use AWS Step Functions, AWS Lambda, and AWS System Manager Parameter Store to automate a configurable ML workflow for Rekognition Custom Labels and A2I. We will provide an overview of the solution and instructions to deploy it with AWS CloudFormation.

Python package that interfaces with AWS System Manager

A Terraform module that generates JSON documents for access for common AWS SSM Parameter Store policies

SecretsFoundry is a package to automatically fetch your environment variables from different sources and secret managers. It makes configuration across environments seamless.

Consul Template plugin for AWS SSM Parameter Store

Configure your CloudFormation managed infrastructure with Parameter Store and CodePipeline

Load your secret information nicely and securely.

Checking out some aws spring cloud libraries

Loads AWS SSM Parameter Store parameters into local system environment variables and then executes your application.

Easily Manage Secrets on AWS using Serverless Components

Go Package for tagging a struct with metadata for getting configuration values from AWS Parameter Store

Manage application configuration stored in cloud services.

boilerplate loads secrets from multiple sources, like AWS Secrets Manager, AWS Parameter Store, Hashicorp Vault, etc.

Load environment variables from AWS Parameter Store

Simple tool to read data from parameter store

Configure your NestJS application with AWS Parameter Store