A brief guide to AIOps and its implementation
Traditional IT management techniques have now become outdated and find it difficult to cope with digital business transformations. ITOps procedures have seen some significant changes in the recent years. They called the evolving platform on which these changes would take place “AIOps.”
Evolution in IT in the past few years have proven that technology needs to change and evolve for betterment. There is an increase in adoption and interest of AIOps as organizations have moved towards enabling innovations, fend off disruptors, and manage the velocity, volume, and variety of digital data that is beyond human scale. This blog covers the basics and the advancements in the AIOps technology, its components and its benefits.
What is AIOps
AIOps is the application of artificial intelligence to IT operations. Now when monitoring and managing modern IT environments has become essential, AIOps provides a hybrid, dynamic, distributed and componentized environment.
AIOps allows algorithmic analysis of IT Ops and DevOps. The teams work smarter and faster, so they can detect digital-service issues earlier and resolve them quickly, before business operations and customers are impacted.
Leveraging AIOps, teams are able to tame the immense complexity and quantity of data generated by their modern IT environments. This prevents outages, maintains uptime and attains continuous service assurance.
IT is the heart of digital transformation efforts and AIOps lets organizations operate at the speed that modern business requires.
An AI Platform for Today — and the Future
The constantly changing, dynamic IT environment has to make use of tools of the present era. Older tools cannot match the technologies that these new tools provide.
IT infrastructures have been seeing an evolution too; from static and predictable physical systems to software-defined resources that change and reconfigure on the fly. It demands equally dynamic technology and processes for their management.
The complexity of managing the operations of modern IT environments exists at three levels:
These complex systems have modular, distributed and dynamic infrastructure at their core that have ephemeral components.
Data comes as the second layer after the core. These data systems generate their internal operations like logs, metrics, traces, event records and more. This data is complex because of its high volume, specificity, variety, redundancy.
The third outer layer encompasses the tools that are used to monitor and manage data and the systems. There are more and more tools, with increasingly narrow functionality, that don’t always interoperate, and thus create operational and data silos.
With the evolution of IT infrastructure, old rules-based systems fall short as they function on a predetermined, static representation of a mostly homogeneous, self-contained IT environment.
AIOps leverages machine learning and data science to give IT operations teams a real-time understanding of any issues, including new, unforeseen problems for which rules haven’t been crafted yet but still affect the availability and performance of digital services.
How Does AIOps Work?
All AIOps products are not created equal. To obtain the maximum value, an organization should deploy it as an independent platform that ingests data from all IT monitoring sources, and acts as a central system of engagement.
This platform is powered by five types of algorithms that fully automate and streamline five key dimensions of IT operations monitoring:
Modern IT environment generates data massive and highly redundant data. With AIOps, you can select the noisy and redundant data and indicate that there’s a problem, which often means filtering out up to 99% of this data.
This feature is for correlating and finding relationships between the selected, meaningful data elements, and grouping them, for further analysis.
Identifying root causes of problems and recurring issues is an essential dimension. You can take action on what has been discovered.
With this parameter, you can notify appropriate operators and teams to facilitate a collaboration with them. This is particularly useful when individuals are geographically dispersed, as well as preserving data on incidents that can accelerate future diagnosis of similar problems.
Automating response and remediation as much as possible, to make solutions more precise and quicker.