AWS Adds ‘ML-Based Analytics’ to Serverless Apps

Amazon DevOps Guru for Serverless uses machine learning to improve operational availability and performance of AWS Lambda applications.
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A service that AWS uses to improve operational availability and performance of AWS Lambda serverless applications through machine learning. ‘Amazon DevOps Guru for Serverless’has revealed Announced on April 21 (local time), this AWS Lambda support is a new feature of the Amazon DevOps Guru service for monitoring application behavior. Amazon DevOps Guru is also available on all Amazon relational database services.

According to the company, Amazon DevOps Guru uses machine learning models based on data from years of AWS and Amazon.com operations to help developers improve application performance. Developers using AWS Lambda can leverage this service to automatically detect anomalous behavior at the functional level and troubleshoot discovered issues through ML-based recommendations. For example, it can detect problems such as under-utilized memory or unprovisioned concurrency, the company said.

When a problem is detected, Amazon DevOps Guru for Serverless displays the result in the DevOps Guru console and sends a notification via Amazon EventBridge or Amazon Simple Notification Service. Developers can navigate the DevOps Guru console to enable the service in Lambda-based applications, other supported resources, or the entire account.

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

  • Triggered when an Amazon Lambda concurrent execution reaches the account limit or when concurrent executions successively reach the account limit.
  • It is set when AWS Lambda runs out of provisioned concurrency capacity, or when the provisioned concurrency reserve for a certain period of time is insufficient.
  • The AWS Lambda timeout is compared to the Visibility Timeout of the Simple Queue Service (SQS), which is triggered when the duration of the Lambda function exceeds the visibility timeout of the event source Amazon SQS.
  • The account read/write capacity of Amazon DynamoDB consumption has reached the account limit.
  • Concurrent usage provisioned for AWS Lambda is lower than expected.
  • The capacity used for the Amazon DaniamoDB table has reached the AutoScaling Max parameter limit.

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Source: ITWorld Korea by www.itworld.co.kr.

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