Harness the power of AI and ML using Splunk and Amazon SageMaker Canvas

This article provides an overview of using Splunk and Amazon SageMaker Canvas together to develop machine learning models for predicting patient hospital admissions by combining data from Splunk (wearable device metrics and logs) and AWS (patient information and admission records).

Specifically, the article covers:

• AWS data engineering pipeline to extract and transform data from Splunk into an Amazon S3 bucket for use with AWS analytics tools
• Using Amazon SageMaker Canvas to import and explore the combined dataset from Splunk and AWS
• Utilizing SageMaker Canvas features like Chat for Data Prep to explore and prepare data using natural language
• Building, testing, and deploying a machine learning model in SageMaker Canvas to predict patient hospital admissions
• Conclusion and benefits of the no-code approach with SageMaker Canvas and generative AI capabilities