Modern manufacturing, transportation, and energy companies routinely operate thousands of machines and perform hundreds of quality checks at different stages of their production and distribution processes. Industrial sensors and IoT devices enable these companies to collect comprehensive real-time metrics across equipment, vehicles, and produced parts, but the analysis of such data streams is a challenging task. In this blog post, we focus on IoT data analysis challenges associated with system health monitoring and how to resolve them. Our main goal is to create an analytical pipeline that: analyzes IoT metrics collected from some physical system; evaluates the probability that the system is in an anomalous state (as opposed to a normal state); and makes binary decisions that can be used to trigger automatic actions or alert operations teams to become involved.

These findings show that cloud adoption and optimization are critical to remain competitive in our ever-evolving digital world. However, while the cloud promises greater efficiency, scalability, cost savings and accelerated innovation that enables new business models and revenue streams, rushed migrations without a strong foundation and a clear strategy can cause the exact opposite. How can enterprises and SMBs achieve the most value from the cloud in practical terms? Let’s dive in and find out!