Many applications meant for industrial equipment maintenance, trade monitoring, fleet management, and route optimization are built using open-source Cassandra APIs and drivers to process data at high speeds and low latency. Managing Cassandra tables yourself can be time consuming and expensive. Amazon Keyspaces (for Apache Cassandra) lets you set up, secure, and scale Cassandra tables in the AWS Cloud without managing additional infrastructure.
In this post, we’ll walk you through AWS Services related to training machine learning (ML) models using Amazon Keyspaces at a high level, and provide step by step instructions for ingesting data from Amazon Keyspaces into Amazon SageMaker and training a model which can be used for a specific customer segmentation use case.
AWS has multiple services to help businesses implement ML processes in the cloud.

AWS ML Stack has three layers. In the middle layer is SageMaker, which provides developers, data scientists, and ML engineers

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