Gartner predicted, by 2020 90% of Artificial Intelligence (AI) and Machine Learning (ML) would have been deployed in enterprises through “AIOps” – a combination of machine learning and operations. 

An AIOps approach has the potential to reduce costs and risks by automating routine IT Operations tasks while returning more control over decisions to the organization. 

This article discusses the benefits of an AIOps strategy, such as increased efficiency and reduced costs, as well as how it can be achieved using AI technologies. 

What is AIOps?  

The term refers to the implementation of AI to management ITOps. It is the process of orchestrating AI algorithms to collect data and generate insights, which can be used to optimize IT operations for better performance. 

 Technology is currently being used across sectors like retail, telecom, BFSI, manufacturing, enterprise software, and many more. It aims to provide better management of IT infrastructures to improve the performance and reliability of IT systems. 

Why was there a need for AIOps? 

With the advent of modern technologies like micro-servers and containerization, it became imperative to move from static systems to software-defined resources, which could be changed and reconfigured on the fly. The resulting complexities that arose could be defined at three levels: System, Data, and tools. Legacy monitoring systems fell short with their static rule-based systems. This is when players in the ITOps domain felt the need for a platform that uses AI and ML to process a large amount of data to give IT teams real-time insight into emerging issues. 

How Does AIOps Works? 

AIOps combines the power of big data analytic, machine learning, and automation to transform IT operations management. 

It is usually composed of three major sub-systems: 

  • The Analytic Subsystem uses AI tools to gather data about the state of the IT environment. 
  • The Machine Learning Subsystem applies algorithms to analyze this data and automatically generate predictions about how it will change in the future. 
  • The Automation Subsystem uses existing processes, policies, and templates to automate tasks that are frequently performed manually. This can be done by either generating scripts for humans to execute or by directly executing them without human intervention. 

AIOps uses big data from various sources that could be in any format, such as:  

  • The system Logs and Metrics 
  • Real-time event data 
  • Network status and traffic data 
  • Tickets and Incident data 
  • Knowledge-based data 

After collecting the data from multiple sources, the solution uses machine learning to provide powerful insights to make decisions and help resolve potential issues. It comes with the capabilities, such as given below.  

  • Noise Reduction: Automatically groups related alerts together and creates meaningful incidents with better context.   
  • Root Cause Analysis: Expedites RCA with better dependency mapping and with drill-down feature into events.  
  • Advanced Automation: Runbook automation to automate configuration management across IT assets using scripting languages like python.  
  • Anomaly Detection: AIOps helps identify the system behavior that falls out of pattern and detect the potential upcoming failures before they cause any harm.  

AIOps Benefits  

AIOps allows IT teams to be agile and responsive to the dynamic nature of the modern IT infrastructure. This ensures optimum digital experience to the end-users and uptime of critical services. Some of the core benefits of AIOps are:  

  • Less downtime: With AIOps, DevOps teams can detect and react to impending issues that might lead to potential downtime. 
  • Better Operational Efficiency: With AIOps, IT teams can pinpoint potential issues and assess their impact on the overall environment. AIOps removes the guesswork from ITOps tasks and provides detailed remediation steps. 
  • Better Skill Management: AIOps offers efficient root cause analysis that can help IT resolve issues faster while simultaneously deepening their skill and understanding. 
  • Better Control: AIOps reduces the need for manual interventions by enabling IT teams to track the differences between IT systems & streamline their monitoring processes. It also simplifies many operations and improves overall stability. 

AIOps Use Cases 

In the real world, AIOps helps IT teams manage the velocity of data generated in a modern IT infrastructure and produce actionable insights. This is done by ingesting heterogeneous data from dispersed sources that include on-premises and cloud infrastructure, virtual instances, storage, and more. The most important use cases of AIOps can be summarized as follows: 

  • Too many monitoring tools make analytics a challenge: Modern enterprises rely on a hybrid model where both on-premises and cloud resources are used. This modern style of architecture presents the challenge of dispersed monitoring tools, which makes it difficult for an enterprise to obtain end-to-end visibility across the infrastructure. An AIOps solution solves this problem by providing a single pane of analysis across an IT infrastructure. 
    Motadata AIOps platform collects data (metrics, log, packet data, etc.) from dispersed sources and processes the same to provide end-to-end insight for on-premises as well as the cloud.   
  • Better manage the velocity of data: Modern hybrid IT infrastructure generates a large amount of data that is not humanly possible to comprehend and process. Here, an AIOps platform leverages its big data capabilities to aggregate data in different formats and performs the analysis. 
    Motadata AIOps platform provides features like anomaly detection, forecasting, and correlation to detect issues proactively, perform alert correlation, and collaborate across teams. 
  • Deliver the best digital experience: AIOps increases IT efficiency and speed of innovation which in turn has a direct impact on the end-user experience. It also has the potential to augment the experience of IT teams through intelligent automation. 
    Motadata AIOps offers capabilities like predictive analytics, impact analysis, unified dashboard, correlation, and runbook automation that allow IT heads to make better decisions and remediate issues that might lead to bigger problems.  

AIOps is undoubtedly evolutionary for all kinds of enterprises. Not just to boost IT operational efficiency, but for business growth as well.