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@diptanu

Building a core feature in @tensorlake's platform to enable functions to store structured data in blob stores. The killer use-case for this is being able to run functions that runs detection/tracking models over camera feeds and store structured data for analytics and querying. Simplifies the architecture of applications by enabling developers to go from video frames to structured data in the same application stack at scale without stitching together queues, lambda functions and GPU inference services. Secondly, storage becomes cheaper as you can query straight from S3 using DuckDB or other query engines instead of writing structured data on SSD backed databases like Postgres if you are not going to query historical data very often. Sampled output form a compute graph running a Yolo v5 model on a GPU running at ~100FPS, supports up to ~90k executors on the same cluster.

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