Crew

Scribble Data’s Session 8 of “Making Data Science Work”

DATA PRIVACY BY DESIGN - Tally Case Study

Panelists:
Dr. Kalpit Desai, Founder, Datakalp

Facilitators:
Indrayudh Ghoshal (Scribble Data)
Venkata Pingali (Scribble Data)

References

Experience at Tally

  • What privacy by design is important? For whom?
  • Why did they choose different paths for analytics and services?
  • How did the privacy considerations impact your modeling roadmap?
  • How did you communicate the approach taken by tally to end-users?
  • What anonymization strategies did they use?

Larger lessons learnt

  • How does privacy change the economics of modeling?
  • How does privacy impact model design and development?
  • How should data scientists prepare for the new regime?
  • Could we audit the models for privacy violations? How should data scientists handle the risk?

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Scribble Data builds feature stores for data science teams that are serious about putting models (ML, or even sub-ML) into production. The ability to systematically transform data is the single biggest determinant of how well these models do. Scribble Data streamlines the feature engineering proces… more

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Hosted by

Scribble Data builds feature stores for data science teams that are serious about putting models (ML, or even sub-ML) into production. The ability to systematically transform data is the single biggest determinant of how well these models do. Scribble Data streamlines the feature engineering proces… more