A key role of AI Ops in managing enterprise application solutions

Enterprise digital transformation has created an IT infrastructure consisting of a huge data warehouse and hybrid and multi-cloud systems. This development has generated huge datasets from different channels, customer touchpoints, and device platforms. Unmatched pace of data generation makes it difficult for organizations to manage IT. This is essential for streamlining operations, increasing monitoring, and pursuing business continuity. Given the limitations of existing IT solutions for managing data, companies are leveraging AI Ops for a variety of activities. These include understanding and predicting customer behavior, detecting anomalies and identifying their reasons, and providing normative advice. Helps detect dependencies that cause problems in your IT infrastructure. In addition, AI with features such as containerization, continuous monitoring, predictive or adaptive cloud management gives businesses a next-generation perspective on their business.

What are AIOps?

It is a software system consisting of big data, machine learning and artificial intelligence that enhances the capabilities of all major IT functions. IT features include automation, IT service management, performance monitoring, event correlation and analysis, and more. In other words, AI Ops is applying data science and machine learning to the DevOps framework to make it more efficient and productive. The benefits of integrating AI into the value chain are:

• Process all types of data generated from different sources quickly and accurately. This ensures data integrity and gives concrete results.

• Analyze large datasets to generate actionable insights (if needed) for DevOps engineers to understand and make infrastructure adjustments.

• Identify the event pattern and set the automatic trigger accordingly.

AI Ops and DevOps: Differences

DevOps is arguably the best software development methodology for speeding up the deployment of high-quality software solutions in any organization. So why are AI Ops an important requirement for businesses? Let’s find out.

• The main difference between AIOps and DevOps is the former multi-layer formation that automates IT operations and enables algorithm analysis on its own. DevOps transformations, on the other hand, include leveraging agile development techniques and using them to automate self-service operations.

• AIOps perform tasks in real time without human intervention. It’s hard to understand with traditional DevOps, and you can analyze and organize IT tasks by data source as well as perform them.

• AIOps can perform a variety of data-driven analytic activities such as streaming data management, historical data management, and log data ingestion. Visualization capabilities enable stakeholders in different business units to view insights.

• DevOps Quality Assurance allows you to automate build deployments using containers and automation tools, but lacks areas such as security and compliance, and system operations.

• While DevOps QA helps streamline SDLC through the CI / CD pipeline, AIOps provides a scalable platform for automating and managing IT operations involving large datasets.

• AI Ops will continue to grow in importance as we need to monitor and manage next-generation enterprise applications running in multiple cloud ecosystems in real time.

Why do companies need to adopt AI Ops?

Building and implementing next-generation enterprise applications involves the use of artificial intelligence and machine learning-driven AI Ops methodologies. The benefits of leveraging this next-generation methodology are:

Eliminate IT noise. IT noise can expose your team to false positives, fill in the root cause event, and make it difficult to detect outages. It can also lead to performance issues, increased operational risk and cost, and denial of enterprise digital initiatives. AIOps-driven tools can reduce or eliminate noise by building correlated incidents that indicate the root cause.

Great customer experience: When the customer experience becomes the most important factor in driving profitability, AI Ops can perform predictive analytics and automate decisions related to future events. By analyzing the data, AI Ops can predict events that will affect the availability and performance of IT systems. In addition, identifying the root cause of IT problems can help you resolve them immediately.

Better collaboration: AIOps can eliminate functional silos and streamline workflows for IT groups and other business units. You can generate customized dashboards and reports to help your team quickly understand tasks and act on them.

Strengthening service provision: AI, ML, and automation help service delivery teams of any enterprise in query resolution by analyzing usage patterns, support tickets, and user interactions. By applying an analysis of possible causes, you can anticipate underlying performance problems and help resolve them.

Conclusion

DevOps test automation is the de facto standard for enabling automation of IT processes, but AI Ops can be a completely different ball game. By minimizing your company’s reliance on specific automation tools, DevOps can properly use the mantle as the next generation of avatars. In addition, AI Ops can monitor the behavior of IT infrastructure and tune data resources to optimize work processes and increase profitability.