What is AIOps (Artificial Intelligence for IT Operations)
The volume of data that IT systems generate nowadays is overwhelming, and without intelligent monitoring and analysis tools, it can result in missed opportunities, alerts, and expensive downtime. However, with the advent of Machine Learning and Big Data, a new category of IT operations tool has emerged called AIOps.
AIOps can be defined as the practical application of Artificial Intelligence to augment, support, and automate IT processes. It leverages Machine Learning, Natural Language Processing, and Analytics to monitor and analyze complex real-time data, helping teams quickly detect and resolve issues.
With AIOps, Ops teams can tame the vast complexity and volume of data generated by their modern IT environments to prevent outages, maintain uptime and achieve continuous service assurance. AIOps enables organizations to operate at speed demanded by modern businesses and deliver a great user experience.
What is the need for AIOps?
In a survey conducted by CA Technologies, most respondents believed that AIOps is the future of IT operations, and more than 80% of the organizations are either planning to or have already started implementing AIOps solutions.
The following are the top five reasons why the necessity of AIOps is increasing.
Analytics has become challenging due to the proliferation of monitoring tools.
Using disparate monitoring tools makes achieving complete visibility across an enterprise service or application difficult. It also makes it nearly impossible to correlate and analyze multiple application performance metrics.
AIOps can help deliver a primary, single pane of analysis across all domains, which will help organizations to ensure an optimal customer experience. AIOps helps reduce false positives, build alert correlation and identify root causes without having the tech go to multiple tools.
The sheer volume of alerts is becoming unmanageable.
With thousands of alerts per month on average that have to be proactively dealt with, it’s no wonder AI and Machine Learning are now becoming necessary. AIOps can help reduce the impact of issues like detecting issues, collaboration across teams, and alert correlation across all tools by reducing downtime and time spent on analyzing these alerts.
Predictive analysis is required to deliver a superior user experience.
Every business today is one lousy user experience away from a lost customer. Considering this, the premium that companies place on ensuring an exceptional user experience is not surprising. Delivering a great user experience with predictive analytics is among the most crucial business outcomes, and as such, predictive analytics is the most sought-after AIOps capability.
Enormous expected benefits of AIOps
Numerous IT professionals believe that AIOps will deliver actionable insights to help automate and enhance overall IT operations functions. They also think AIOps will increase efficiency, faster remediation, better user experience, and reduce operational complexity. This is primarily achieved through AIOps’ automation capabilities, including automating data analytics and predictive insights across the entire toolchain.
The future of IT operations is AIOps.
Businesses that want to survive and thrive in today’s digital economy must consider using AI in IT operations. With increasing data monitoring and analytics challenges, AIOps will play a key role in creating new efficiencies for IT Ops teams. Now is the time to evaluate and implement AIOps-based solutions that deliver the superior user experience that customers expect.
How does AIOps work, and what are its components?
An organization should deploy an AIOps tool to extract maximum values as an independent platform that takes data from all IT monitoring sources. Such a platform should be powered by five algorithms that automate and streamline critical dimensions of IT operations monitoring.
- Data Selection: Taking a vast amount of highly redundant and noisy data generated by modern IT environments and filtering out the data elements that indicate an issue.
- Pattern Identification: Correlating and finding relationships between the selected data elements and grouping them for further analysis.
- Inference: Identifying the leading causes of recurring issues so that action can be taken.
- Collaboration: Notifying relevant operators and teams and facilitating cooperation between them.
- Automation: Automating response and remediation to make solutions more precise and quick.
AIOps solutions filter out noise and duplication in the dataset and select only the relevant data. This greatly reduces the number of alerts the operations team has to deal with and eliminates duplication of work. The relevant information is then grouped and correlated using various criteria such as text, time, and topology. AIOPS then discovers patterns in the data and infers which data items represent causes and which data items represent events.
The platform sends the results of this analysis to a virtual collaboration environment where all relevant data is accessible to everyone involved in resolving the incident. The virtual team can then quickly determine solutions and choose automated responses to resolve incidents quickly and accurately.
AIOps use cases
Root cause analysis
With AIOps, a problem’s root cause can be determined, and appropriate measures can be taken to solve it. By identifying the cause of the issue, the team can avoid unnecessary work involved in treating the problem’s symptoms rather than the core problem. For example, AIOps platforms can track the cause of network outages, fix them immediately, and take protective measures to prevent similar issues in the future.
AIOps tools can scan large datasets and discover atypical data points. These outliers act as signals that identify and predict problematic events, such as data breaches, allowing businesses to avoid costly consequences, such as regulatory fines, negative PR, and declines in consumer confidence.
AIOps acts as a monitoring tool for cloud infrastructure and storage systems. It reports on metrics such as usage, availability, and response times. It also uses event correlation to aggregate information, leading to better information consumption for users.
AIOps filters and correlates meaningful data into incidents preventing alert storms from domino effects- for example, a failure in one system triggers an alert, impacting another system which also triggers an alert.
AIOps helps automate remediation for known issues. Once the problems are identified, based on historical data from past issues, AIOps suggests the best approach to accelerate remediation.
What is the difference between AIOps and MLOps?
|It is a set of practices for better communication and collaboration between data scientists and operations professionals.||It is the practical application of Artificial Intelligence to augment, support, and automate IT processes.|
|This discipline combines machine learning, data engineering, and DevOps to uncover faster and more effective ways to deploy machine learning models.||It combines big data and machine learning to automate IT operations.|
|Through dataset validation, application monitoring, reproducibility, and experiment tracking, MLOps makes it possible to efficiently get models into production and ensure they continue functioning reliably.||AIOps systems identify the root causes of IT incidents, detect anomalies, and provide high-quality solutions that enable the tech teams to work towards a resolution.|
I am a Civil Engineering Graduate (2022) from Jamia Millia Islamia, New Delhi, and I have a keen interest in Data Science, especially Neural Networks and their application in various areas.