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Why DevOps and AIOps Are Decisive for Enterprises?

Blog: Think Data Analytics Blog

Enterprises are evolving with technology and creating large amounts of data to monitor and identify any concerns that may arise in their IT infrastructure. By providing a system for controlling and automating IT monitoring, DevOps and AIOps have enabled IT Ops teams to re-manage IT areas, rapidly discover, and fix problems, and avert a shutdown.

What Exactly is DevOps?

DevOps is a phrase that combines the concepts Dev (development) and Ops (operations). DevOps is a collection of people, technologies, and procedures that work together to provide customers with a unique experience. To put it another way, DevOps is a set of procedures, platforms, and cultural ideas that automate and merge software development and IT team practices.

Alternatively, it promotes flexibility, efficiency, and agility while also maintaining IT infrastructure in preparation for future advancements and deployments. It prioritized team motivation, improved communication, seamless collaboration, and technical error automation.

What Exactly is AIOps?

AIOps aka Artificial Intelligence for IT Operations includes AI and machine learning for IT operations. It refers to how an IT team uses AI technology to handle data and information from an IT environment. It’s the ITOps analytics of the future. It combines a vast amount of data and uses machine learning to automate IT functions such as event-related, confusion detection, and cause determination.

With AIOps, businesses will be able to monitor five different types of algorithms:

Data Collection: AIOps identifies data attributes that suggest a problem from a massive volume of undesirable and noisy IT data created by a modern IT system, typically filtering up to 99 percent of the data.

Pattern Discovery: For further analysis, linking and discovering relationships between selected, logical, and connected data elements.

Smooth Inference: Discovers the underlying factors of incidents, identifies problems, and identifies recurring difficulties so that enterprises can act on the information.

Quick Collaboration: Alerts key operators and teams, increases their collaboration, particularly when employees are globally scattered, and maintains event data that might speed the detection of related issues in the future.

Automation: As much as possible, automate response and cleanup to save time using the help of Machine Learning and big data.

AIOps is a technique of automating processes using machine learning skills. It also aids businesses in getting a clear understanding of their IT infrastructure. DevOps, on the other hand, emphasizes seamless collaboration and communication between developers and operations teams across the Software Development Life Cycle. Let’s check out the differences between AIOps and DevOps.

Difference between DevOps and AIOps?

DevOps is an excellent technique to improve your IT Operations, allowing you to build and deliver faster. The DevOps methodology aids development and performance teams in forming partnerships based on a shared passion to achieve organizational objectives. IT teams can work together quite well if DevOps principles are applied.

AIOps is a platform for automating IT operations and auto-remediation using AI models. This automated approach accelerates DevOps-driven IT transformations and increases the organization’s agility.

With modern traditional commercial enterprise systems running across various cloud providers and requiring real-time insight, the usefulness of AIOps will grow.

Why is combining AIOps and DevOps beneficial?

AIOps integrates seamlessly with a variety of tools and processes, allowing teams to utilize the numerous datasets created by various apps and infrastructure. All these separated data sources are processed and analyzed by AIOps to comprehend the numerous connections within the data and to properly monitor the system to always ensure appropriate functioning.

AIOps provides several benefits to DevOps teams and developers, as well as IT Ops. AIOps’ self-healing functionality, integration capabilities with expert bots, and supervising end-to-end users have all shown to be advantageous, saving time for developers, improving efficiency, and cutting request turnaround times. The most essential aspect of AIOps is that it generates an atmosphere where code is always set to be released.

AIOps is increasingly being included in DevOps systems, particularly log ingestion, analytics, and code risk assessment. In the future, AIOps will go beyond pre-production measurements to incorporate production indicators such as user engagement, quality, and business relevance in the DevOps framework. All of this suggests that DevOps teams who use AIOps platforms to monitor and maintain apps will be able to reduce development time and costs.

Traditional performance and service-management techniques and tools are being pushed to their limits by all these new technologies and users. The ideal technique for the IT operations staff to deal with these digital transformation concerns is AIOps. AIOps increases the potential of IT operations by applying automated and AI-based analytics to a wide range of data ingested into a modern and open observability platform, freeing teams to focus on operational excellence and aiding the company’s transition to a self-driving digital enterprise.


 Thanks to AIOps, ITOps will be able to intelligently orchestrate infrastructure, apps, and services across hybrid cloud ecosystems to align with business goals and respond to consumer needs on-demand. Enterprises must understand the requirement of digital transformation for the entire IT systems to enable their enterprise to satisfy the needs of the fast-moving digital market.

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