Data Science Operationalization: What the heck is it?
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.…
