~/talk

CATs: Content-Addressable Transformers

Talkat PyData NYC 2022 · 2022-11-09..11-11· [machine-learning, web3]
links:none on file
slides:none on file

Claims an open-source framework that uses IPFS CIDs to content-address inputs processes outputs and IaC, orchestrates Spark workloads via Kubernetes (demo on Minikube), and records a Bill of Materials / invoice as a verifiable provenance chain so processes can be re‑executed. Demo builds a virtualenv, does spark-submit, writes CID'd partitions and a BOM, then replays a subsequent CAT on the network using an IPFS cache workaround; repo named cats targets Ubuntu 20.

AI-written summary · about

  1. 0:00Introduction to CATs
  2. 1:48Why Content-Addressable Transformers are useful
  3. 3:36Decentralizing monoliths
  4. 5:06IPFS for verification
  5. 6:10Defining data products
  6. 7:54Data-driven design
  7. 9:00BOM as provenance
  8. 10:05CATs I/O pipeline
  9. 11:30Quantum principle
  10. 12:30Control planes
  11. 14:17Interoperable infrastructure
  12. 16:05CATs execution
  13. 17:07Kubernetes setup
  14. 18:34Executing CAT zero
  15. 19:32Demo: Deploy CAT
  16. 21:18Invoice and provenance
  17. 22:32Demo: Process data
  18. 24:18Generating results
  19. 26:22Acknowledgments
  20. 26:58Q&A
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