DevOps vs. DataOps : The Process Difference Explained

DataOps: A new revolutionary trend to come in 2019

DevOps: A perfect starting point for DataOps

How to capitalize on the DataOps innovations?

The difference between DevOps and DataOps processes

  • Data engineers are responsible for data collection and curation. These data sets are further utilized for evaluating ML algorithms and training Data Science models.
  • Data scientists are the best at using programming languages like SAS, R or SQL as well as AI-backed frameworks like TensorFlow, Theano, Keras to develop seamless ML and Deep Learning models.
  • Architects and software developers make use of the models generated by data scientists to create full-fledged applications.
  • Chief data officers (CDOs) are in charge of IT security and data governance. They provide full or partial access to the pools of historical data for data scientists, in regard to the tasks they need to accomplish.
  • The DevOps specialists come into action to support the entire software development lifecycle. They hold responsibility for the automation of processes, application deployment, testing and eventual production releases.

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