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2021 Trends in Data Science: The Entire AI Spectrum Posted on : Dec 01 - 2020

As an enterprise discipline, data science is the antithesis of Artificial Intelligence. The one is an unrestrained field in which creativity, innovation, and efficacy are the only limitations; the other is bound by innumerable restrictions regarding engineering, governance, regulations, and the proverbial bottom line.

Nevertheless, the tangible business value praised by enterprise applications of AI is almost always spawned from data science. The ModelOps trend spearheading today’s cognitive computing has a vital, distinctive correlation within the realm of data scientists.

Whereas ModelOps is centered on solidifying operational consistency for all forms of AI—from its knowledge base to its statistical base—data science is the tacit force underpinning this motion by expanding the sorts of data involved in these undertakings.

Or, as Stardog CEO Kendall Clark put it, “If companies want to win with data science they really have to take seriously the breadth and diversity of all the types of data, not just the ones that are amenable to statistical techniques.”

By availing themselves of the full spectrum of data at their disposal, organizations can explore the boundaries of data science to master intelligent feature creation, explainability, data preparation, model standardization and selection—almost all of which lead to palpable advantages for enterprise deployments of AI.

Intelligent Feature Generation

What Clark termed “perceptual or computer visible” machine learning data directly invokes AI’s statistical foundation. Building machine learning models is predicated on identifying features that enhance model accuracy for applications of computer vision, for instance, to monitor defects in an assembly line process in the Industrial Internet. According to Gul Ege, SAS Senior Director of Advanced Analytics, Research and Development, “Intelligent feature creation comes from what is important to the domain and how we process this data.” Some of the numerous methods for enriching feature identification involve:

  • Peaks and Distances: Ege outlined an EKG wearables device use case in which streaming data comes in cyclical patterns. When discerning features to see if patients are afflicted with specific heart diseases conditions, for instance, “You apply noise reduction, and then look at the cyclic patterns and apply analytics to find the peaks and measure the distance between the peaks,” Ege explained. “The feature is the distance between the peaks.”
  • Simplified Queries: Entity event models in graph settings supporting AI’s knowledge base greatly simplify the schema—and abridge the length of queries to traverse them—to represent an endless array of temporal events pertaining to critical entities like customers, patients, or products. According to Franz CEO Jans Aasman, “If you have a complex graph without entity event models, then if you want to extract features for machine learning, you have to write complex queries. With this approach, you write simple queries to get the data out.” View More