Note: A webinar that draws on this presentation is available here.

In this report, we discuss the Data Science workflow, from gathering requirements for the activity through to deploying the output and monitoring it. This workflow is amenable to automation, a topic increasingly referred to as DataOps. DataOps is a sibling of DevOps for Data Science, which aims to allow more effective industrialisation of Data Science in practice, through:

• Improved repeatability of findings
• Reduced time to identifying actionable insights
• Decreased time to impact