课程: Advanced AI Analytics on AWS: Amazon Bedrock, Q, SageMaker Data Wrangler, and QuickSight

免费学习该课程！

今天就开通帐号，24,700 门业界名师课程任您挑！

Course summary and next steps

Course summary and next steps

课程: Advanced AI Analytics on AWS: Amazon Bedrock, Q, SageMaker Data Wrangler, and QuickSight

开始一个月试用 购买团队方案

Course summary and next steps

“

- [Instructor] Today I'm going to walk through a visualization of an AWS AI-powered code optimization pipeline. This pipeline leverages AWS services, like Amazon Bedrock, Amazon Queue, SageMaker, Data Wrangler and QuickSight to analyze, optimize and visualize code performance. Let's dive into each stage of this pipeline. First up in the pipeline overview here, this pipeline consists of four key stages and they're powered by different AWS services. The flow starts with code analysis, then it moves to intelligent code transformation, and then it goes to processing performance data, and then ends with visualization and insights. And these animated lines are connecting the stages, representing the seamless flow of data insights between these services. If we look at Amazon Bedrock code analysis, this first stage is powered by Amazon Bedrock. The AWS code analysis engine here is Claude, and it's going to look at the code base to identify inefficiencies and areas of improvement. And the code…

内容