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#cloud #monitor... #observa... #AIOps #DevOps
The unprecedented growth of data in recent years has led to a demand for evolution in traditional monitoring practices.
The current observability maturity model is a good solution but needs further augmentations.
The widely accepted model includes the following stages:
1) Monitoring (Is everything in working order?)
2) Observability (Why is it not working?)
3) Full-Stack Observability (What is the origin of the problem, and what are its consequences?)
4) Intelligent Observability (How to predict anomalies and automate response?)
LOGIQ is supporting the next stage in the model i.e, Federated Observability. In other words, data availability for consumers with on-demand convenience.
#aws #gitops #iac #AIOps #DevOps
With the rising adoption of cloud technologies in the world, Devops has been key in enhancing efficiencies in development and operations. Studies show that the DevOps market is to reach close to $13 billion by 2025.DevOps is considered as a journey and not a destination, a shift in culture and not t..
#ITOps #MLOps #observa... #AIOps #DevOps
What is meant by Observability and how has it evolved into something which has transformed the entire IT ecosystem. We will break down all the information in this blog. Before Agile methodology was born, developers also used something called Test-Driven Development
#DevOps #MLOps #AIOps #Machine...
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.
AIOps is the use of artificial intelligence to make IT operations management simple, accelerate the time to solve IT operations problems by automating their resolution. This post lists the most popular AIOps open source tools.