Artificial intelligence for IT operations (AIOps) is a process where you use artificial intelligence (AI) techniques maintain IT infrastructure. You automate critical operational tasks like performance monitoring, workload scheduling, and data backups. AIOps technologies use modern machine learning (ML), natural language processing (NLP), and other advanced AI methodologies to improve IT operational efficiency. They bring proactive, personalized, and real-time insights to IT operations by collecting and analyzing data from many different sources.
Why is AIOps important?
When your organization modernizes your operational services and IT infrastructure, you benefit when you ingest, analyze, and apply increasingly large volumes of data. Next, we share several key business advantages of using an AIOps platform.
Reduce operational costs
AIOps allows your organization to derive actionable insights from big data while maintaining a lean team of data experts. Equipped with AIOps solutions, data experts augment IT teams to resolve operational issues with precision and avoid costly errors.
Moreover, AIOps allows IT operation teams to spend more time on critical tasks instead of common, repetitive ones. This helps your organization to manage costs amidst increasingly complex IT infrastructure while fulfilling customer demands.
Reduce problem mitigation time
AIOps provides event correlation capabilities. It analyzes real-time data and determines patterns that might point to system anomalies. With advanced analytics, your operation teams can conduct efficient root-cause analysis and resolve system issues promptly. This maximizes service availability.
Meanwhile, ML algorithms separate noise from data sources. So, your IT engineers can focus on important events.
Enable predictive service management
With AIOps, your organization can anticipate and mitigate future issues by analyzing historical data with ML technologies. ML models analyze large volumes of data and detect patterns that escape human assessments. Rather than reacting to problems, your team can use predictive analytics and real-time data processing to reduce disruptions to critical services.
Streamline IT operations
In a conventional setup, IT departments have to work with disparate data sources. This slows down business operation processes and might subject organizations to human errors.
AIOps provides a common framework for aggregating information from multiple data sources. With AIOps, your IT teams can collaborate and coordinate workflows without human intervention, which improves productivity.
Elevate customer experience
AIOps tools can analyze large amounts of information from chats, emails, and other channels. Some companies use AIOps platforms to analyze customer behavior and improve service deliveries.
AIOps also prevents costly service disruptions from affecting customers. Your organization can provide an optimal digital customer experience by ensuring service availability and effective incident management policy.
Support cloud migration
AIOps provides a unified approach to managing public, private, or hybrid cloud infrastructures. Your organization can migrate workloads from traditional setups to the cloud infrastructure without worrying about complex data movements on the network. It improves observability, so your IT teams can seamlessly manage data across different storage, networks, and applications.
What are some AIOps use cases?
AIOps combines machine learning, big data, and analytics. It helps your IT and operational teams to support digital transformation initiatives.
Application performance monitoring (APM)
Modern applications use complex software technologies to run and scale across the cloud environment. It’s challenging to gather metrics with traditional methods from modern scenarios—like data exchanges between components like microservices, APIs, and data storages.
Instead, software teams adopt AI for application performance monitoring to gather and compile relevant metrics at scale.
Root cause analysis
AI/ML technologies are efficient in helping you determine the root cause of an incident. They rapidly process big data and correlate between multiple probable causes. By adopting AIOps, your organization can investigate beyond symptoms or alerts to the true causes impacting system performance.
Anomaly detection
Anomalies are outliers deviating from the standard distribution of monitored data. They often indicate abnormal behaviors that affect system operations. AIOps provides real-time assessment and predictive capabilities to quickly detect data deviations and accelerate corrective actions.
With AIOps, your IT teams reduce dependencies on system alerts when managing incidents. It also allows your IT teams to set rule-based policies that automate remediation actions.
Cloud automation and optimization
AIOps solutions support cloud transformation by providing transparency, observability, and automation for workloads. Deploying and managing cloud applications requires greater flexibility and agility when managing interdependencies. Organizations use AIOps solutions to provision and scale compute resources as needed.
For example, you can use AIOps monitoring tools to compute cloud usage and increase capacities to support traffic growth.
App development support
DevOps teams use AIOps tools to improve code quality. They can automate code review, apply programming best practices, and detect bugs earlier in the development stages. Rather than delegating quality checks to the end of the development cycle, AIOps tools shift quality checks to the left.
For example, Atlassian uses Amazon CodeGuru to reduce investigation time from days to hours or minutes when anomalies occur in production.
How does AIOps work?
With AIOps, your organization takes a more proactive approach to resolve IT operational issues. Instead of relying upon sequential system alerts, your IT teams use machine learning and big data analytics. This breaks down data silos, improves situational awareness, and automates personalized responses to incidents. With AIOps, your organization is better able to enforce IT policies to support business decisions.
Next, we discuss interconnected AIOps phases.
Observe
The observe phase refers to the intelligent collection of data from your IT environment. AIOps improves observability amongst disparate devices and data sources across your organization’s network.
By deploying big data analytics and ML technologies, you can ingest, aggregate, and analyze massive amounts of information in real time. An IT operations team can identify patterns and correlate events in log and performance data. For example, businesses use AI tools to trace the request path in an API interaction.
Engage
The engage phase involves using human experts to resolve issues. Operations teams reduce their dependencies on conventional IT metrics and alerts. They use AIOps analytics to coordinate IT workloads on multicloud environments. IT and operational teams share information with a common dashboard to streamline efforts in diagnosis and assessment.
The system also raises personalized and real-time alerts to the appropriate teams. It does this both preemptively and in case of incidents.
Act
The act phase refers to how AIOps technologies take actions to improve and maintain IT infrastructure. The eventual goal of AIOps is to automate operational processes and refocus teams’ resources on mission-critical tasks.
IT teams can create automated responses based on the analytics that ML algorithms generate. They can deploy more intelligent systems that learn from historical events and preempt similar issues with automated scripts. For example, your developers can use AI to automatically inspect codes and confirm problem resolution before they release software updates to affected customers.
What are the types of AIOps?
AIOps creates new possibilities for your organization to streamline operations and reduce costs. There are, however, two types of AIOps solutions that cater to different requirements.
Domain-centric AIOps are AI-powered tools designed to function within a specific scope. For example, operational teams use domain-centric AIOps platforms to monitor networking, application, and cloud computing performance.
Domain-agnostic AIOps are solutions that IT teams can use to scale predictive analytics and AI automation across network and organizational boundaries. These platforms collect event data generated from multiple sources and correlate them to provide valuable business insights.
What is the difference between AIOps and other related terms?
AIOps is a relatively new concept that promotes the use of machine learning and big data processing to improve IT operations. Here’s how it compares to several related terms.
AIOps vs. DevOps
DevOps is a software practice that bridges the gap between development and support workflows. It helps organizations apply changes and quickly address users’ concerns by sharing information between software and operations teams.
On the other hand, AIOps is an approach for using AI technologies to support existing IT processes. DevOps teams use AIOps tools to assess coding quality and reduce software delivery time continuously.
AIOps vs. MLOps
MLOps is a framework that helps software teams integrate ML models into digital products. It involves model selection and data preparation. It includes the process where you train, evaluate, and deploy the ML application in the production environment.
Meanwhile, AIOps is the application of ML solutions to generate actionable insights and improve the process efficiency of new and existing IT systems.
AIOps vs. SRE
Site reliability engineering (SRE) is an approach that engineering teams can use to automate system operations and perform checks with software tools. Instead of relying on manual approaches, SRE teams improve software reliability and customer experience by automatically detecting and resolving issues.
AIOps shares overlapping goals with SRE. It uses business operations’ massive data and ML-sourced predictive insights to help site reliability engineers reduce incident resolution time.
AIOps vs. DataOps
DataOps is an initiative that allows organizations to optimize data usage for business intelligence applications. It involves setting up data pipelines that data engineers can use to ingest, transform, and transfer data from different domains to support business operations.
Meanwhile, AIOps is a more complex practice. It uses information that DataOps provides to detect, analyze, and resolve incidents.
