AIOps

Topic: 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Traditional IT management techniques have now become outdated and find it difficult to cope with digital business transformations.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:

Systems level:

These complex systems have modular, distributed and dynamic infrastructure at their core that have ephemeral components.

Data:

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.

Tools:

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:

Data selection

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.

Pattern discovery

This feature is for correlating and finding relationships between the selected, meaningful data elements, and grouping them, for further analysis.

Inference

Identifying root causes of problems and recurring issues is an essential dimension. You can take action on what has been discovered.

Collaboration

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.

Automation

Automating response and remediation as much as possible, to make solutions more precise and quicker.

Summary

AIOps is like a seismic change for IT operations. It’s not a radical application of analytics and machine learning. A machine learning approach similar to ML, was implemented when stockbrokers moved from manual trading to machine trading. Analytics and ML are also used in social media and in applications like Google Maps, Waze, and Yelp, as well as in online marketplaces like Amazon and eBay. These techniques are reliable especially in environments where real-time responses to dynamically-changing conditions and user customization are required.

Social media write up: The IT world has exploded and evolved in an unforeseen way and has generated immense data that now needs to be tamed. IT experts face a lot of challenges in doing so, and hence keep evolving the data analysing and structuring technologies. AIOps is a fully evolved ML technology that supports data gathering and analysis and addresses the ever-changing customer demands.

Quora questions

1. How AIOps (artificial Intelligence for IT Operations) will disrupt IT Operations Management? Gartner, Inc was the first to coin the term AIOps refer to it as the integration of IT and Artificial Intelligence in Operation Management. The AIOps platform utilizes modern machine learning, visualization and advanced analytics technologies with Big Data. This all, to enhance IT Operations functions like monitoring, automation and service desk.

In 2016, the trend of introducing artificial intelligence operating system (AIOS) and Machine learning in everything possible led to the birth of AIOps in first place by the Gartner Research analyst Colin Fletcher.

AIOps typically have these key functional layers:

• Automation
• Storage
• Visualisation
• Machine Learning
• Analytics offering Correlation
• Prediction

Why does your IT team need to care about AIOps?

The software and applications are getting increasingly complex with the surfacing of every new solution. Eventually, every enterprise needs to use multiple cloud providers and applications to affiliate to its various services necessary – the increasing variety and amount of data generation. Human monitoring alerts tend to be slow and errorful. Without any supervision, machine learning algorithms analyze what alerts regarding Big data are real.

How one can implement AIOps (artificial Intelligence for IT operations)?

Artificial Intelligence (AIOps) for IT Operations is an emerging technology that basically focuses on the use of Machine Learning, Big Data, and Artificial Intelligence for improving IT Operations. AIOps for IT reduces the time of work, saves cost, and identifies and fix the IT issues.

To implement AIOps, in an organization it is first mandatory to know what level of maturity is your organization at. Here are the various levels of maturity of an organization -

• Knee-Jerk: Here the event logs are generated in the silos and collected from several devices and applications in the infrastructure. These are used for generating alerts.

• Unified: All the alerts, events, and logs are integrated to one central locale, hence the process of ITSM is unified. This helps in breaking the silos and in return, the business impacts are better tackled by the engineering team.

• Intelligent: To derive insights, machine learning algorithms are implemented on the unified data. For future events, there are baseline metrics that would be calibrated and used. Metrics gets richer with more data.

• Predictive and autonomous: The application availability can be improved by leveraging Artificial Intelligence to predict incident performance and degradation of applications. Based on predictive insights, autonomous remediation bots are triggered. This fixes the incidents which are prone to happen to enterprises. This step is considered as the paradigm shift when it comes to the implementation of AI in IT operations. Depending on the above levels, an organization can assess its maturity in adopting AIOps. The digital advancement and the requirement for cloud-based workplaces and tools have dragged Artificial Intelligence into IT operations for achieving better productivity and higher quality of work.