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    Home»Machine Learning»Serverless at Scale: Moving From Polling to Event-driven Design | by Eyal Sol | Converge Bio | Jun, 2025
    Machine Learning

    Serverless at Scale: Moving From Polling to Event-driven Design | by Eyal Sol | Converge Bio | Jun, 2025

    Team_AIBS NewsBy Team_AIBS NewsJune 11, 2025No Comments1 Min Read
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    Initially our “naive” resolution was a easy polling logic each x minutes
    Checking if successful/failure file was created by SageMaker.

    Based mostly on a file existence or not — we knew what inference occasion(s) succeeded/failed and up to date their corresponding job standing:

    Yep .. That Easy

    Sadly, the Implementation by the AWS SageMaker staff has edge-cases — It’s doesn’t create a “failure” file for the next situations:

    • Occasions timing out inside AWS managed queue of person requests
    • Didn’t end processing inside timeout restrict (e.g max 1h)

    This left us BLIND for some uncommon but trivial edge-cases inflicting us:

    • couldn’t discover a success/failure file
    • couldn’t perceive the end result of an inference activity
    • couldn’t resolve if a job was completed

    This was the second we understood we wanted an structure change.



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