- Privacy: Running on edge means data doesn't have to be sent back to servers and can power local, privacy-first machine learning, such as federated learning or executing models on private PII information.
- Low-Latency: Interacting with client-side data avoids round-trip of server back and forth and allows for computation to be performed on the client's device itself. You can also consider a combination of low-latency local decisions combined with slower, longer round-trip for more powerful models running in the cloud that are queried at a different time scale.
- Run Anywhere: No need to worry about user's OS or user interaction, and no server costs or scaling when exploring demos with users.
This repository was archived by the owner on Feb 25, 2026. It is now read-only.
googleinterns/paksha
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