Gartner defines DataOps as a collaborative data management practice focused on improving the communication, integration, and automation of data flows between data managers and data consumers across an organization. DataOps provides governance and extracts value and the resulting metadata to augment the usability and data values in changing environments. It is achieved by automating the various stages in the process, such as design, deployment, and management. Ultimately, the goal is to maximize value creation and establish predictable delivery.
Data pipelines can be very complex, and to build successful pipeline deployments, we need every step along the way to be understood.
Sunil outlines the challenges of DataOps. Additionally, he discusses the challenges of data pipelines.
Join us in the podcast and hear more about the challenges of ML training data.
A link to this podcast “Utilizing AI” episode can be found here, or click on the video below.