Managers of DevOps and Agile teams will need to address the need for computational analysis of data that is too big or moves too quickly for existing processing aptitudes.

Overview DevOps speeds up software delivery while ensuring stability and reliability in applications.MLOps manages models and data to maintain accuracy, fairnes ...

Devops requirements for data scientists differ from application developers Not every organization may be ready to invest in data science platforms, or it may have small data science teams who only ...

Machine learning brings a new dimension to DevOps. Along with developers, operators will have to collaborate with data scientists and data engineers to support businesses embracing the ML paradigm.

Ask any CTO or CIO about DevOps and data science, and they’ll say that smart enterprises are investing in expertise for both skill sets. The DevOps approach has made IT more responsive to business ...

The legacy data quality tools were never designed (or intended) to serve as quality control tools for today’s complex continuous data pipelines that carry data in motion from application to ...

Delphix’s DevOps Data Platform—composed of data virtualization, data compliance, and data automation—delivers this new approach, accelerating app releases with highly secure, ephemeral environments ...

Data architectures are becoming more complex and changing more frequently, requiring that a new operational mindset be applied to data management. In particular, automating the building and ...

For this article, I consulted with industry experts to identify what devops leaders and teams should know about data governance and how they can contribute to its goals.

MLOps vs. DevOps: Why data makes it different @vtuulos @hugobowne October 30, 2021 2:20 PM Image Credit: MF3d/Getty Images ...