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AutoML 2.0: Is The Data Scientist Obsolete?

By Ryohei Fujimaki, PhD.

As originally seen on Forbes Cognitive World, our CEO - Ryohei Fujimaki PhD was a primary contributor to this article.  In case you've missed it, we've reposted the Original Article below. It's an AutoML WorldThe world of AutoML has been proliferating over the past few years - and with a recession looming, the notion of automating the development of AI and Machine Learning is bound to become even more appealing. New platforms are available with increased capabilities and more automation. The advent of AI-powered Feature Engineering - which allows users to discover and create features for data science processing automatically - is enabling a whole new approach to data science that, seemingly, threatens the role of the data scientist. Should data scientists be concerned about these developments? What is the role of the data scientist in an automated process? How do organizations evolve because of this newfound automation?AutoML 2.0, More Automation for…

Data Science Operationalization: What the heck is it?

By Walter Paliska

Data Science Operationalization Defined Data science operationalization, in concept, is simple enough: Take Machine Learning (ML) or Artificial Intelligence (AI) models and move them into production (or operational) environments. In the words of Gartner Sr. Analyst Peter Krensky, data science operationalization is the "...application and maintenance of predictive and prescriptive models..." In practice, however, operationalizing ML and AI models can be a complicated and often overwhelming challenge. In a broader concept, one of the biggest challenges of operationalization is that AI and ML models get integrated with systems that contain live data that changes quickly. For example, if your model is designed to predict customer churn, your data science operationalization process needs to be integrated with your CRM system to predict churn effectively as your data volumes grow. What makes data science operationalization so hard? There are four critical aspects of data science operationalization that make it challenging to implement.…