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DevOps monitoring tools: How to drive DevOps efficiency

Dynatrace

With the world’s increased reliance on digital services and the organizational pressure on IT teams to innovate faster, the need for DevOps monitoring tools has grown exponentially. But when and how does DevOps monitoring fit into the process? And how do DevOps monitoring tools help teams achieve DevOps efficiency?

DevOps 220
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Is DevOps dead? Exploring the changing IT landscape and future of DevOps

Dynatrace

Just as organizations have increasingly shifted from on-premises environments to those in the cloud, development and operations teams now work together in a DevOps framework rather than in silos. But as digital transformation persists, new inefficiencies are emerging and changing the future of DevOps.

DevOps 162
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Shift left vs shift-right: A DevOps mystery solved

Dynatrace

The DevOps approach to developing software aims to speed applications into production by releasing small builds frequently as code evolves. As part of the continuous cycle of progressive delivery, DevOps teams are also adopting shift-left and shift-right principles to ensure software quality in these dynamic environments.

DevOps 196
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How platform engineering and IDP observability can accelerate developer velocity

Dynatrace

As organizations look to expand DevOps maturity, improve operational efficiency, and increase developer velocity, they are embracing platform engineering as a key driver. The pair showed how to track factors including developer velocity, platform adoption, DevOps research and assessment metrics, security, and operational costs.

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Applying real-world AIOps use cases to your operations

Dynatrace

Thus, modern AIOps solutions encompass observability, AI, and analytics to help teams automate use cases related to cloud operations (CloudOps), software development and operations (DevOps), and securing applications (SecOps). DevOps: Applying AIOps to development environments. A huge advantage of this approach is speed.

DevOps 196
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What is artificial intelligence? See how it differs from machine learning in IT ops

Dynatrace

Machine learning algorithms use vast amounts of data to train systems and allow them to draw accurate conclusions based on available information. Why deterministic AIOps is essential for DevOps — and beyond. For more information about developing an AIOps strategy for cloud observability and how Dynatrace can help, read our eBook.

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What is AIOps? Everything you wanted to know

Dynatrace

Here, we’ll discuss the AIOps landscape as it stands today and present an alternative approach that truly integrates artificial intelligence into the DevOps process. Modern AIOps enables more comprehensive automation across the enterprise, including in CloudOps, DevOps, and SecOps. A huge advantage of this approach is speed.