You work as a machine learning specialist for a mobile phone operator where you need to build a machine learning model that predicts when a given customer is about to leave your phone service or churn.
The inference data produced by your model will allow your marketing department to offer incentives to the customer to get them to stay with your service.
Using data generated by customer activity with your service offering, you need to visualize the inference data in a dashboard.
So your marketing department can quickly decide which customer churn candidates to offer additional incentives.
How can you get your machine learning inference data into your dashboard visualization in the most efficient, performant manner?
Click on the arrows to vote for the correct answer
A. B. C. D.Correct Answer: B.
Option A is incorrect.
Although you could use Athena and S3 to visualize your data as this option describes, it is not as efficient as using the Augment with SageMaker feature of QuickSight.
Option B is correct.
You can use the Augment with SageMaker feature of QuickSight to integrate your SageMaker inference data into your QuickSight visualization.
This is by far the most efficient option given.
Option C is incorrect.
Although you could use S3 Analytics and S3 to visualize your data, as this option describes, it is not as efficient as using the Augment with SageMaker feature of QuickSight.
Option D is incorrect.
Although you could use Redshift Spectrum and S3 to visualize your data, as this option describes, it is not as efficient as using the Augment with SageMaker feature of QuickSight.
You would have to create a Redshift cluster for this option to work.
References:
Please see the AWS Machine Learning blog titled Visualizing Amazon SageMaker machine learning predictions with Amazon QuickSight (https://aws.amazon.com/blogs/machine-learning/making-machine-learning-predictions-in-amazon-quicksight-and-amazon-sagemaker/),
The AWS Samples GitHub repo titled ML Predictions using Amazon QuickSight and Amazon SageMaker (https://github.com/aws-samples/quicksight-sagemaker-integration-blog)
To efficiently and performantly visualize the machine learning inference data in a dashboard, option A is the most appropriate choice.
Option A: As your inference engine produces potential churn candidate data, write the data to S3. Use Athena to query the data and associate a QuickSight visualization data source with your Athena query results.
Explanation:
Write data to S3: As the inference engine produces potential churn candidate data, the first step is to write the data to S3. S3 is an object storage service that is highly available, durable, and scalable. The churn candidate data can be stored in S3 as objects, making it easy to access and query the data later.
Use Athena to query data: Athena is a serverless, interactive query service that makes it easy to analyze data in S3 using standard SQL. It is an ideal choice for querying large datasets stored in S3, and it can process structured, semi-structured, and unstructured data. Using Athena, you can create a table to map the churn candidate data stored in S3 to a structured format that can be easily queried using SQL.
Associate a QuickSight visualization data source: Once the churn candidate data is available in Athena, you can create a QuickSight data source that points to the Athena query results. QuickSight is a fully-managed, cloud-powered business intelligence service that makes it easy to create and publish dashboards. It offers a variety of visualization options, including tables, charts, and graphs, which can be customized to suit your needs. With a QuickSight data source connected to your Athena query results, you can quickly create a dashboard that visualizes the churn candidate data.
Overall, using S3 to store the churn candidate data, Athena to query the data, and QuickSight to visualize the data is a performant and efficient way to build a dashboard that helps the marketing department offer incentives to customers who are at risk of churning.