Looking at the report that Gartner did in 2022 regarding top technology trends, AI engineering represents an important pillar in the near future. It is composed of three core technologies: DataOps, MLOps and DevOps.The discipline’s main purpose is to develop AI models that can quickly and continuously provide business value. For instance, models that enable cross-functional collaboration, automation, data analysis, and machine learning.
On 8 November 2022, at Open Source Experience Paris, Canonical announced that Charmed Kubeflow, Canonical’s enterprise-ready Kubeflow distribution, now integrates with MindSpore, a deep learning framework open-sourced by Huawei. Charmed Kubeflow is an end-to-end MLOps platform with optimised complex model training capabilities designed for use with Kubernetes.
In an era where there’s no shortage of established best practices and tools, engineering teams are consistently finding their ability to prevent, detect and resolve production issues is only getting harder. Why is this the case? Our most recent DevOps Pulse Survey highlighted alarming trends to this end.
I’m proud and humbled to announce ServiceNow is a Leader in the 2022 Gartner® Magic Quadrant™ for IT Service Management (ITSM) Platforms for the ninth year in a row. We also ranked first in all three use cases evaluated in the 2022 Critical Capabilities for ITSM Platforms: Service Desk (3.76/5), Service Operations (3.73/5), and Business Workflow Automation (3.81/5).
Let's be honest, nobody loves surveys. Ok, well I sure don't. But surveys satisfy a huge need in our demand for insights into complex human-computer, sociotechnical systems. It turns out that we've been measuring the computer part pretty well, but the humans – not as easy to keep track of. When Google SRE first defined toil as a metric we wanted to reduce, we spent far too long trying to quantify it numerically based on tooling and insights from computer systems.
These three top cloud providers are used daily by millions of users across the world – but what sets them apart? We take a magnifying glass to the features that matter.
