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MLS-C01 · Question #122

A manufacturer is operating a large number of factories with a complex supply chain relationship where unexpected downtime of a machine can cause production to stop at several factories. A data scient

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ML Implementation and Operations

Question

A manufacturer is operating a large number of factories with a complex supply chain relationship where unexpected downtime of a machine can cause production to stop at several factories. A data scientist wants to analyze sensor data from the factories to identify equipment in need of preemptive maintenance and then dispatch a service team to prevent unplanned downtime. The sensor readings from a single machine can include up to 200 data points including temperatures, voltages, vibrations, RPMs, and pressure readings. To collect this sensor data, the manufacturer deployed Wi-Fi and LANs across the factories. Even though many factory locations do not have reliable or high-speed internet connectivity, the manufacturer would like to maintain near-real-time inference capabilities. Which deployment architecture for the model will address these business requirements?

Options

  • ADeploy the model in Amazon SageMaker.
  • BDeploy the model on AWS IoT Greengrass in each factory.
  • CDeploy the model to an Amazon SageMaker batch transformation job.
  • DDeploy the model in Amazon SageMaker and use an IoT rule to write data to an Amazon

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Topics

#Edge ML#AWS IoT Greengrass#Real-time Inference#Disconnected Operations
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