How can AIOps improve Cloud Migration?

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Cloud is becoming more and more of a vital component in many organizations’ digital transformation strategies. With the current world crisis, the process of cloud migration has even been sped up in order to give customers the best digital experiences possible.

However, cloud migration can still be a challenge. This is why using AIOps can ease the process all the while giving businesses more visibility and efficiency to achieve the digital transformation to a cloud-native architecture.

 

What is AIOps?

Artificial intelligence for IT operations (AIOps) is an umbrella term for the use of big data analytics, machine learning (ML), and other artificial intelligence (AI) technologies in order to automate the identification and resolution of common IT issues. Using AIOps in large enterprises can provide them with immense volumes of log and performance data, which help monitor assets and gain visibility into dependencies without and outside of IT systems.

Hence, AIOps can automate routine practices, including user requests and non-critical IT system alerts, as well as evaluate the alert to determine if it requires an action or not depending on its relevance. AIOps can also recognize serious issues quicker and better than humans by looking out for threats in the systems that IT professionals might miss. Finally, AIOps can streamline the interactions between data center groups and teams by analyzing and monitoring the relevant data.

 

AIOps in Cloud Migration

Cloud migration is a critical operation for businesses and one of the best ways to mitigate the risks is to use AIOps.

Before the move to the cloud happens, AIOps can evaluate the potential impact of the migration on the IT environment. IT teams can then identify and resolve issues before they start to disrupt the end consumer during a cloud migration as well as get insights in multi-cloud and containerized environments. That way, the old and the new operational data will be prepared through algorithmic noise reduction and alert correlation.

Once the move to the cloud is done, ML and AI can check which resources are underused and identify which ones are the best for the company, hence limiting the costs and unnecessary resources.

AIOps also provides clear visibility across the platform, thus making it easier to manage the data in the cloud as well as suggests the best optimization strategies. Using a cloud migration strategy with AIOps can help identify what needs to be changed or not in order to keep the organization at the top of the competition.

As there is more and more reliance on the cloud, integrating AIOps into businesses’ IT environment will only improve the performance of the end consumer.

 

Conclusion

With the increase in digital transformations, AIOps is also rising in relevance. If organizations implement it into their strategy, transformations will happen quicker and easier. Data will only become more difficult to handle manually, hence making AI and ML essential.

AIOps seems to be a vital solution for a balanced digitalization and modernization of operational workflows.

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