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

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Performance pipeline integration with GenAI

Performance pipeline integration with GenAI

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

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Performance pipeline integration with GenAI

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- [Presenter] Today we're going to talk about a critical challenge in Cross Language Performance Analysis. When you're comparing Python and Rust implementations you need to have precise, data-driven instrumentation, systematic data collection, and robust analysis. And in this solution, I leveraged AWS Services and also GenAI to create and end-to-end performance analysis pipeline. So first up here in Data Collection, under the top left, the bass profiler is providing this unified time stamp generation. And the Cross Language Instrumentation captures this Fibonacci 40 number. The CSV output has millisecond precision timestamps so I can use this later in analysis. And there's 10 iterations per language, so that I have a statistical control. If we look at the QuickSight data prep here in the top center, a few things you have to do when you're dealing with traditional data prep, is call on transformation for timestamp precision. Also the language categorization setup, and then execution…

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