Topic 1 Question 112

AWS Certified Machine Learning - Specialty

Topic 1 Question 112
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?
- Deploy the model in Amazon SageMaker. Run sensor data through this model to predict which machines need maintenance.
- Deploy the model on AWS IoT Greengrass in each factory. Run sensor data through this model to infer which machines need maintenance.
- Deploy the model to an Amazon SageMaker batch transformation job. Generate inferences in a daily batch report to identify machines that need maintenance.
- Deploy the model in Amazon SageMaker and use an IoT rule to write data to an Amazon DynamoDB table. Consume a DynamoDB stream from the table with an AWS Lambda function to invoke the endpoint.
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- I would select B, based on the following AWS examples: https://aws.amazon.com/blogs/iot/industrial-iot-from-condition-based-monitoring-to-predictive-quality-to-digitize-your-factory-with-aws-iot-services/ https://aws.amazon.com/blogs/iot/using-aws-iot-for-predictive-maintenance/
  
  👍 22
  joep212021/09/20
- B is my answer.
  
  For latency-sensitive use cases and for use-cases that require analyzing large amounts of streaming data, it may not be possible to run ML inference in the cloud. Besides, cloud-connectivity may not be available all the time.
  
  For these use cases, you need to deploy the ML model close to the data source.
  
  SageMaker Neo + IoT GreenGrass
  
  To design and push something to edge:
  
  design something to do the job, say TF model
  
  compile it for the edge device using SageMaker Neo, say Nvidia Jetson
  
  run it on the edge using IoT GreenGrass
  👍 16
  SophieSu2021/10/02
- 正解だと思う選択肢: B
  This solution requires edge capabilities and to be able to run the inference models in near real-time. SageMaker Neo is a deployable unit on the edge architecture (IoT Greengrass) which can host the runtime inference model.
  
  👍 4
  passionatecricketer2021/11/18
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