In 2020, companies accelerated the adoption of artificial intelligence (AI) and machine learning (ML), focusing on projects that increase revenue and reduce expenditures. Today, all businesses recognize ML and AI as critical strategic areas in the future of computing. Companies today are investing more than ever in discovering how ML/AI can give them an edge. As a result of digitization, data has exploded exponentially for decades. Managing and analyzing data has been extraordinarily difficult. This article by William L. Bain at Transforming Data with Intelligence speaks about the importance of machine learning in the world of analytics.
Currently, there are over 10 billion IoT devices in use worldwide, and there are projected to be more than 25 billion devices by 2025, generating an unimaginable 73.1 ZB (zettabytes) of data. Making it impossible for humans to track, analyze, and spot trends or anomalies even in the fraction of data generated by these devices.
Benefits of Machine Learning
One of the examples is managing a fleet of haul truck. Currently, fleet managers track thousands of trucks simultaneously and are aware of cargo status and driving behavior through cloud-hosted telematics software. Even with these advanced technologies, it is challenging to identify issues on the horizon beforehand. It is important to track incoming telemetry data to automatically identify actionable issues. The challenge of creating algorithms that can uncover new problems hidden in telemetry streams is daunting, to say the least.
Machine learning (ML) can help tackle these problems. By studying thousands of historic telemetry messages classified as abnormal or normal, you can train an ML algorithm to recognize abnormal patterns. Overall, you can achieve this without coding.
Once deployed, the ML algorithm will examine incoming data within milliseconds of its arrival. And then log abnormal events and/or notify personnel when necessary. Streaming analytics platforms must be able to handle the influx of data from thousands of sources (such as trucks) at scale.
This is where the software ‘real-time digital twins’ comes into play. In “real-time digital twins,” software components are assigned to each physical data source that runs on memory processing platforms in real-time. Furthermore, they provide powerful new ways to run ML algorithms in real-time and at scale.
With machine learning implemented in real-time digital twins, streaming analytics takes a significant step forward. Be it unlocking new capabilities, notifying situational awareness proactively, and making better-informed decisions.
To read the original article, click on https://tdwi.org/articles/2022/01/13/adv-all-machine-learning-to-power-the-future-of-streaming-analytics.aspx