MLOps vs AIOps
There is a tendency to confuse MLOps and AIOps. While there are some common characteristics between the two, MLOps and AIOps are two different domains, are applied differently, and serve different goals.
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There is a tendency to confuse MLOps and AIOps. While there are some common characteristics between the two, MLOps and AIOps are two different domains, are applied differently, and serve different goals.

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Cloud configurations change. All the time. It’s futile to imagine web app development without a constant stream of configuration changes in order to adopt new technologies, release new features, and support new business requirements.

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Learn about the encrypted version introduced with 5.1 to handling multi-environment credentials before rails 6 to Handling multi-environment credentials in rails 6 here

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It is nothing good or bad to artificial intelligence(AI) it will simply respond with results that are derived completely by its learning. The good or badness of AI will depend on how well we train the AI, and perhaps most importantly how well we test the artificial intelligence.What is Artificial In..

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For this last part of 2021 we have a few interesting free events within DevOps, Cloud, and Application Modernization industry:

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Security is a crucial part of application development and DevSecOps makes it easy and continuous.
The videos in this article will give a thorough breakdown of what DevSecOps is, why you need it, and how you can integrate it into your development lifecycle.

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Testing is an integral part of modern application development. Using the various tools in this article, you can achieve a stable and highly performant application.

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kind and K3s are Kubernetes tools that leverage containers to provide flexible and lightweight Kubernetes distributions. This article highlights the features of both tools and the subtle differences between them.
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Monitoring and observability are two closely related concepts that help IT professionals understand and trouble their systems. This article explains the important component of the two concepts, focusing on observability to differentiate between the two.

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MLOps aims to make machine learning workflows more flexible, scalable, and manageable. The tools highlighted in this article are some of the best managed tools available in the MLOps market to help achieve this in your workflow.
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