Confidential Computing; DataOps and MLOps

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Get Ready For Confidential Computing

A comprehensive data privacy and security policy involves protecting the confidentiality and integrity of data in any of these three states: at rest, in use, and in transit. In a new post with Intel Capital’s Assaf Araki, we describe the ecosystem of tools focused on protecting data while in use. Our primary focus is on Confidential Computing tools for the development of data, analytic, and AI applications. We believe that companies that are able to use data securely will be well-positioned to build data and AI applications in the future.

Safeguarding data while it’s being used is particularly challenging because most applications need to have data in the clear – unencrypted or otherwise protected – in order to compute. The field of Confidential Computing encompasses tools and techniques such as hardware, cryptography, algorithms, and machine learning:

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Data Exchange podcast

  • Why You Need a Modern Metadata Platform: Pardhu Gunnam and Mars Lan, are co-founders of Metaphor Data, creators of the first Modern Metadata Platform (MMP). They explain why a metadata fabric is the right foundation for data governance and data discovery solutions, data catalogs, and other enterprise data services required for a strong DataOps practice.
  • MLOps Anti-Patterns: Nikhil Muralidhar is a Graduate Research Assistant at the Virginia Tech College of Engineering, and is the lead author of an excellent recent survey paper entitled “Using AntiPatterns to avoid MLOps Mistakes”.  Nikhil and his co-authors provide a vocabulary for anti-patterns encountered in ML pipelines, with a focus on the financial services industry.

[Key Phrases found in Job Postings that contain “data engineer”.  Click HERE for fill size version.]

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