With the increasing popularity of AI, new frontiers are emerging in predictive maintenance and manufacturing decision science. However, there are many complexities associated with modeling plant assets, training predictive models for them, and deploying these models at scale, including:

Generating failure data, which can be difficult to obtain, but physical simulations can be used to create synthetic data with a variety of failure conditions.

Ingesting high-frequency data from many sensors, where time-alignment makes it difficult to design a streaming architecture.

This talk will focus on building a system to address these challenges using MATLAB^®, Simulink^®, Apache™ Kafka^®, and Microsoft^® Azure^®. You will see a physical model of an engineering asset and learn how to develop a machine learning model for that asset. To deploy the model as a scalable and reliable cloud service, we will incorporate time-windowing and manage out-of-order data with Apache Kafka.