Using Python to Power Serverless Applications

on YouTube ↗01h 02m

" summary

Capital One shows Python powering API, streaming and ML-inference workflows with concrete artifacts: a GitHub sample project (pycon2023), local testing via AWS SAM and Docker, deployment to AWS Lambda and EFS, and observability wired to CloudWatch and X‑Ray. Code uses Lambda Power Tools, pydantic/fastjsonschema, and pytest. Demonstrated scale from 10 TPS to 4000 TPS, cautions about >5 MB payloads, and measured simple handlers at ~17 ms; demos build, deploy and observe successfully.

AI-written summary · about

" chapters (10)

1. 0:00Introduction to serverless with Python
2. 2:29Why Python excels for serverless
3. 10:48Serverless mindset and trade-offs
4. 16:23Observability: logging and metrics
5. 22:19AWS Lambda Powertools best practices
6. 25:31Local dev with SAM CLI
7. 31:06Input validation and testing
8. 40:47Deploying to AWS
9. 43:17Observability in deployed serverless applications
10. 59:55Key takeaways

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