You work for a city government in their shared bike program as a machine learning specialist.
You need to visualize the bike share location predictions you are producing on an hourly basis using your model inference you created using the SageMaker built-in K-Means algorithm.
Your inference endpoint takes IoT data from your shared bikes as they are used throughout the city.
You also want to enrich your shared bike data with external data sources such as current weather and road conditions. Which set of Amazon services would you use to create your visualization with the least amount of effort?
Click on the arrows to vote for the correct answer
A. B. C. D.Answer: A.
Option A is correct.
IoT Core collects data from each shared bike, IoT Analytics retrieves messages from the shared bikes as they stream data, IoT Analytics also enriches the streaming data with your external data sources and sends the streaming data to your K-Means machine learning inference endpoint, QuickSight is then used to create your visualization.
This approach requires the least amount of effort mainly because of the data enrichment feature of IoT Analytics.
Option B is incorrect.
With this option, you would have to create a lambda function to gather the data enrichment information (weather, road conditions) and enrich the data streams in your own code.
Option C is incorrect.
Also, with this option, you would have to add code to your lambda function to gather the data enrichment information (weather, road conditions) and enrich the data streams in your own code.
Option D is incorrect.
IoT Greengrass is a service that you use to run local machine learning inference capabilities on connected devices.
This approach would not easily integrate with your QuickSight visualization.
Reference:
Please see the AWS IoT Analytics overview, the Amazon SageMaker developer guide titled K-Means Algorithm, the AWS Big Data blog titled Build a Visualization and Monitoring Dashboard for IoT Data with Amazon Kinesis Analytics and Amazon QuickSight, the AWS IoT Analytics User Guide titled What IS AWS IoT Analytics?, and the AWS IoT Greengrass FAQs.
To create a visualization of the bike share location predictions produced using a SageMaker built-in K-Means algorithm, along with external data sources such as current weather and road conditions, the following Amazon services can be used:
B. IoT Core -> Kinesis Firehose -> SageMaker -> QuickSight
Here's a detailed explanation of why this option is the most appropriate:
IoT Core: This is used to ingest the IoT data from the shared bikes as they are used throughout the city.
Kinesis Firehose: This service is used to collect and deliver real-time streaming data to destinations such as data stores, Amazon S3 buckets, and Amazon Redshift. In this case, Kinesis Firehose can be used to collect the data from IoT Core and deliver it to the next service, SageMaker.
SageMaker: This service is used to perform machine learning inference on the data collected from IoT Core and Kinesis Firehose. In this case, the SageMaker built-in K-Means algorithm can be used to produce bike share location predictions.
QuickSight: This service is used to visualize the data produced by the machine learning model. In this case, QuickSight can be used to create a dashboard that visualizes the bike share location predictions, along with external data sources such as current weather and road conditions.
Option A is not the most appropriate because IoT Analytics is not necessary to achieve the desired outcome, and it adds unnecessary complexity to the solution. Option C is not the most appropriate because Lambda is a compute service and not a data processing or visualization service. Option D is not the most appropriate because IoT Greengrass is used for edge computing and not data processing or visualization.