Here’s an interesting article from CodeProject¬†defining the cycles of data science and how it relates to business cycles and the fairly well established framework of SDLC. Although some will argue that data science is “pure science” and this cycle belongs to the “data engineering” label, organizations that fail to move innovations efficiently from “the lab” to production are not going to be competitive.

By its simple definition, Data Science is a multi-disciplinary field that contains multiple processes to extract knowledge or useful output from Input Data. The output may be Predictive or Descriptive analysis, Report, Business Intelligence, etc. Data Science has well-defined lifecycles similar to any other projects and CRISP-DM and TDSP are some of the proven standards.

Donovan Brown and Gopi Chigakkagari discuss how to integrate Azure Pipelines with various 3rd party tools to achieve full DevOps cycle with Multi-cloud support. You can continue to use you existing tools and get Azure Pipelines benefits: application release orchestration, deployment, approvals, and full traceability all the way to the code or issue.


  •      

    Related resources: