Enterprises have struggled to move beyond sandbox exploratory data science in their organization into actionable data science that is embedded in their production applications. Those challenges can be organizational or technological.
Organizational challenges usually result from data science teams that are unable to communicate with other parts of the organization. This may manifest as a lack of cooperation and communication between engineering and data teams; there may be no processes in place to integrate data science insights into production application. Engineers might be brought into the discussion once models are already written, while data scientists may not be trusted with access to production systems or the creation of production-oriented applications.
Data science teams may have problems integrating insights into production if the team lacks the appropriate experience. Having only data scientists with modeling skills, but without data engineering, DevOps engineering, or development skills, is a recipe for conflict. Data science teams need to be able to understand production system requirements, constraints, and architecture, and factor those into the packaging, provisioning, and operation of their “production deployed” analytics workflows, models, or applications.
Technological challenges, too, make it difficult to bring data science models to production. Organizations where the engineering and data science teams use a disjoint combination ...