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The use of AI and machine learning is growing rapidly as businesses go through digital transformation. Models and data flow to become more challenging to manage as they become more complicated. The fact that MLOps and AIOps are still fairly new fields of study presents another difficulty.

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.

While MLOps and AIOps are quite separate disciplines involving various technologies and procedures, both machine learning and artificial intelligence play a significant role in aiding businesses in achieving operational efficiency. Most importantly, they accomplish distinct objectives.

By automating incident management and machine monitoring using machine learning, AIOps improve the effectiveness of IT operations. Putting ML models into production is known as MLOps. To put models into production more quickly it makes it simpler to bridge the gap between data operations and infrastructure teams. MLOps doesn’t specifically refer to an ML capability, in contrast to AIOps.

In other words, MLOps standardizes processes whereas AIOps automates machines.There are parallels in the teams and abilities needed to properly execute AIOps and MLOps, despite the obvious distinctions. It is worthwhile to consider where they intersect before focusing on one or the other to determine which resources can support both disciplines. For instance, both the MLOps and AIOps processes can be sped up with a comprehensive ModelOps platform that has ready-to-deploy models.

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Current Focus Areas: IT Services, AIOps, 5G, Cloud, Project Management. Also specialises in Application Rationalization, Cost Optimization, Benchmarking, Report writing, and Market Research.

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