AWS unveils ML-powered devops for AWS Lambda


Amazon Web Services (AWS) has unveiled Amazon DevOps Guru for Serverless, a service that uses machine learning to improve the operational availability and performance of AWS Lambda serverless applications.

Introduced April 21, the AWS Lambda support is a new feature of the Amazon DevOps Guru service for monitoring application behaviors. Amazon DevOps Guru is also available for all Amazon Relational Database Services.

Amazon DevOps Guru uses machine learning models informed by years of AWS and operations to help developers improve application performance. Developers using AWS Lambda can use the service to automatically detect anomalous behavior at the function level and use ML-powered recommendations to remediate any found issues. Problems can be detected such as underutilization of memory or low-provisioned concurrency.

When an issue is detected, Amazon DevOps Guru for Severless displays findings in the Devops Guru console and sends notifications via Amazon EventBridge or Amazon Simple Notification Service (SNS). To get started, developers can navigate the DevOps Guru console to enable the service for Lambda-based applications, other supported resources, or an entire account.

Specific operational issues and proactive insights available from Amazon DevOps Guru include:

  • AWS Lambda concurrent executions reaching account limit, triggered when concurrent executions reach an account limit for a continuous period.
  • AWS Lambda provisioned concurrency function limit breached, set off when the reserve amount of provisioned concurrency is insufficient over a period.
  • AWS Lambda timeout high compared to Simple Queue Service’s visibility timeout, triggered when the duration of the Lambda function exceeds the visibility timeout for the event source Amazon Simple Queue Service (Amazon SQS).
  • Account read/write capacity for Amazon DynamoDB consumption is reaching the account limit.
  • AWS Lambda provisioned concurrency usage is lower than expected.
  • Amazon DynamoDB table consumed capacity reaching the AutoScaling Max parameter limit.

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