Organizations are increasingly building and using machine learning (ML)-powered solutions for a variety of use cases and problems, including predictive maintenance of machine parts, product recommendations based on customer preferences, credit profiling, content moderation, fraud detection, and more. In many of these scenarios, the effectiveness and benefits derived from these ML-powered solutions can be further enhanced when they can process and derive insights from data events in near-real time.
Although the business value and benefits of near-real-time ML-powered solutions are well established, the architecture required to implement these solutions at scale with optimum reliability and performance is complicated. This post describes how you can combine Amazon Kinesis, AWS Glue, and Amazon SageMaker to build a near-real-time feature engineering and inference solution for predictive maintenance.
Use case overview
We focus on a predictive maintenance use case where sensors deployed in the field (such as industrial equipment or network devices), need to

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