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An Introduction to Graph Data

This article is an excerpt from the book Machine Learning with PyTorch and Scikit-Learn from the best-selling Python Machine Learning series, updated and expanded to cover PyTorch, transformers, and graph neural networks.

Broadly speaking, graphs represent a certain way we describe and capture relationships in data. Graphs are a particular kind of data structure that is nonlinear and abstract. And since graphs are abstract objects, a concrete representation needs to be defined so the graphs can be operated on. Furthermore, graphs can be defined to have certain properties that may require different representations. Figure 1 summarizes the common types of graphs, which we will discuss in more detail in the following subsections:
Common types of graph

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How Machine Learning Helps Analytics Be More Proactive

"Many organizations claim that their business decisions are data-driven. But they often use the term "data-driven" to mean reporting key performance metrics based on historical data — and using analysis of these metrics to support and justify business decisions that will, hopefully, lead to desired business outcomes. While this is a good start, it is no longer enough".

The traditional role of data and analytics has always been in supporting decision-making. Now, they are applied where they have never been before. Today data and analytics are not only used for describing, diagnosing, predicting, or even recommending the best actions but also triggering those actions automatically. The motivation behind this new area of application is the goal of many businesses to reduce task performance time and the volume of human labor.

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Using Machine Learning to Remotely Log Asset Performance

For global manufacturing enterprises or other industries that rely on automated machinery across locations, the ability to keep tabs on asset performance becomes crucial. While manual supervision has worked well in such scenarios, there is a definite opportunity to optimize costs here. That's by enabling virtual monitoring and logging of asset performance data. 

Our team recently built a solution for this use case using machine learning solutions from AWS. It was designed to remotely capture video on machine performance and create logs of when the asset/machine was in operation and for how long.