Monitoring and managing a DevOps environment is complex. The volume of data generated by new distributed architectures (such as Kubernetes) makes it difficult for DevOps teams to effectively respond to customer requests. By Hicham Bouissoumer, Nicolas Giron.
The future of DevOps must therefore be based on intelligent management systems. Since humans are not equipped to handle the massive volumes of data and computing in daily operations, artificial intelligence (AI) will become the critical tool for computing, analyzing, and transforming how teams develop, deliver, deploy, and manage applications.
Further in the article:
- What are Machine Learning operations?
- Lifecycle of a Machine Learning model
- Core elements of MLOps
- What are Artificial Intelligence operations?
- Core element of AIOps
- AIOps toolset
- What is the difference between MLOps and AIOps?
Coupled with the increasing complexity of architectures of modern applications, the demands of this digital economy have made the role of IT operations much more complex. As a result, ML and AI have emerged to automate some manual business processes to increase efficiency. Organizations throughout the world are increasingly looking to automation technologies as a means of improving operational efficiency. This indicates that tech leaders are becoming more and more interested in MLOps and AIOps. Good read!
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