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

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Building an intelligent code transformation pipeline

Building an intelligent code transformation pipeline

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

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Building an intelligent code transformation pipeline

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- [Instructor] Today we're going to explore an intelligent pipeline that transforms the Python code into an optimized Rust version and leverages Gen AI and AWS services. You can see here that first you start with Python, you then convert it with DeepSeek or Claude, go to Rust. Now here's where the interesting thing comes into play, where what you may want to do as well is start to instrument your code. And once you instrument your code, maybe the Python version and the Rust version, because you believe in data science, you then put those artifacts into S3. The next thing you do is you may want to then use Claude or Bedrock to do some kind of analytics. Maybe you come up with a forecast or you do some kind of pre-processing or summary. But once you're done with that, you may want to go even further. And this is where you could use a tool like QuickSight to do a realtime visualization. So if we think about this, what's interesting is that there are many different touch points for…

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