Openapi Tf Example
Example of how you can use OpenAPI with AWS API Gateway, Also includes integrations with AWSLambda, AWS Cognito, AWS SNS and CloudWatch logs
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Archive_aws Lambda Go Event | 77 | 2 | 6 years ago | 3 | November 29, 2017 | 3 | apache-2.0 | Go | ||
| Type definitions for AWS Lambda event sources. | ||||||||||
| Event Mocks | 67 | a year ago | 19 | TypeScript | ||||||
| Jrestless Examples | 28 | 6 years ago | 4 | apache-2.0 | Java | |||||
| JRestless Examples | ||||||||||
| Serverless Cognito Oauth2 | 27 | 4 years ago | 3 | JavaScript | ||||||
| Serverless Cognito OAuth2 authentication module | ||||||||||
| Openapi Tf Example | 22 | 3 years ago | 5 | apache-2.0 | HCL | |||||
| Example of how you can use OpenAPI with AWS API Gateway, Also includes integrations with AWSLambda, AWS Cognito, AWS SNS and CloudWatch logs | ||||||||||
| Awsmeter | 20 | 4 months ago | 4 | apache-2.0 | Java | |||||
| JMeter plugin to execute load test over Kinesis Data Stream, SQS Standard and FIFO Queues, SNS Standard and FIFO Topics, Cognito AWS services. | ||||||||||
| Amazon Rekognition Custom Labels A2i Automated Continuous Model Improvement | 13 | 2 years ago | other | Python | ||||||
| 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. | ||||||||||
| Ansible.missing_collection | 6 | 2 years ago | 32 | other | Python | |||||
| Ansible Collection of Missing Modules. | ||||||||||
| Sqsswift | 5 | 5 years ago | apache-2.0 | Swift | ||||||
| Swift 4.2 client for sending single and batch messages directly to Amazon SQS | ||||||||||
| Okky | 5 | 6 years ago | Vue | |||||||
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