The challenge with most machine learning and cognitive platforms is that they take a significant amount of effort to configure and teach before an organization can receive any real value from them. It is the very broad-based nature of these platforms that causes them to require so much work, as each organization must essentially start from scratch.
SpaceTime Insight solves this problem for asset-intensive industries such as energy, utilities, and transportation. They do so by creating purpose-built machine learning applications that sit on top of their Warp 6 platform, which includes built-in data integration and correlation (including IoT support), analytics and visualization layers.
These applications aim to help organizations detect anomalies and predict the impending failure of critical (and often very expensive) assets. Once a likely failure is detected, the machine learning platform also helps organizations optimize their operational support.
For example, these applications can help an organization evaluate the business cost of an asset failure, the cost of repair and other critical factors to determine both the optimal time for a field visit as well as assignment options and priorities. Moreover, because of the company’s intense focus on these industries and its use of unsupervised learning techniques, the company can rapidly deploy outcome-driven solutions that deliver rapid time-to-value.
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