Remove Best Practices Remove Database Remove Software Engineering Remove Training
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Cloudy with a high chance of DBMS: a 10-year prediction for enterprise-grade ML

The Morning Paper

Many of the software engineering discipline and controls need to be brought over into an ML context. The following chart breaks down features in three main areas: training and auditing, serving and deployment, and data management, across six systems. But model inference migrating into the DBMS is a bolder prediction.

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USENIX LISA 2018: CFP Now Open

Brendan Gregg

Today's LISA attracts attendees working on all sizes of production systems, and its attendees include sysadmins, systems engineers, SREs, DevOps engineers, software engineers, IT managers, security engineers, network administrators, researchers, students, and more. Hope to see you in Nashville!

DevOps 43
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USENIX LISA 2018: CFP Now Open

Brendan Gregg

Today's LISA attracts attendees working on all sizes of production systems, and its attendees include sysadmins, systems engineers, SREs, DevOps engineers, software engineers, IT managers, security engineers, network administrators, researchers, students, and more. Hope to see you in Nashville!

DevOps 40
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MLOps and DevOps: Why Data Makes It Different

O'Reilly

As with many burgeoning fields and disciplines, we don’t yet have a shared canonical infrastructure stack or best practices for developing and deploying data-intensive applications. Can’t we just fold it into existing DevOps best practices? How can you start applying the stack in practice today?

DevOps 138