As the head of Global Commercial Operations for Uptake, Ajay Madwesh created a five-part video series on thought leadership. This is the fourth installment of the thought leadership series discussing the Application of AI/ML in Operational Environments.
Read the video transcript for your convenience:
Often, you see systems integrators and even clients think of just throwing data scientists at a problem. These data scientists may have statistical backgrounds, customer behavior backgrounds, etc. They have a set of tools. They know Python, and they know Scala. They know a bunch of different programming scripting and programming languages. But the nature of Data Sciences is to build AI/ML models in the operation space is very different.
For example, we think of the ability to do machine learning-based anomaly detection.
You’re thinking about millions of data points coming in and doing simple clustering analysis; segmentation analysis is not sufficient because the underlying data sets have something about called a state. Each of these plants may operate in different states at different points in time.
Those data sets coming in embody that change in state. You must extract that state before applying machine learning to that environment.
Fusion provides that data and context. The context allows the AI/ML models or any of its front and filtering to extract features that are state-oriented and features that are not stated-oriented. This is an advantage to clients who want to build advanced models on the operational side of the world. That’s an important position for us at Fusion to take, and also, that’s an important position for the clients to understand. That’s one of the many things we explain as to why Fusion fits into their environment much better.
