What is AIOps?

Artificial intelligence for IT operations (AIOps) is a process where you use artificial intelligence (AI) techniques to maintain IT infrastructure. This process automates critical operational tasks, including 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. These technologies provide proactive, personalized, and real-time insights into IT operations by collecting and analyzing data from multiple sources.

When your organization modernizes your operational services and IT infrastructure, you ingest, analyze, and apply increasingly large volumes of data to improve operations through AI. Here are several key business advantages of using AIOps for operations management.

Reduce operational costs

AIOps allows your organization to derive actionable insights from big data while maintaining a small team of data experts. Equipped with AIOps solutions, data experts augment IT teams to resolve operational issues with precision and avoid costly errors.

AIOps allows IT operations teams to spend more time on critical tasks rather than on common, repetitive tasks. This helps your organization manage costs amid increasingly complex IT infrastructure while meeting customer demands.

Reduce problem mitigation time

AIOps provides event correlation capabilities. It analyzes real-time data and identifies patterns that may indicate system anomalies. With advanced analytics, your operations teams can conduct efficient root-cause analysis and resolve system issues promptly. This maximizes service availability.

Effective ML algorithms separate noise from data sources, so that your IT engineers can focus on important events.

Enable predictive service management

Using 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 human assessments miss. 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 operations processes and might introduce human errors.

AIOps provides a common framework for aggregating information from multiple data sources. With AIOps, your IT teams can collaborate and coordinate workflows with minimal human intervention, which improves productivity.

Elevate customer experience

AIOps tools can analyze large amounts of information from chats, emails, and other channels. Organizations use AIOps solutions to analyze customer behavior and improve service delivery.

AIOps also prevents costly service disruptions from affecting customers. Your organization can provide an optimal digital customer experience by ensuring service availability and implementing an effective incident management policy.

Support hybrid cloud and cloud migration

AIOps provides a unified approach to managing public, private, or hybrid cloud infrastructures. Your organization can migrate workloads from traditional setups to cloud infrastructure while supporting complex data movements across the network. It improves observability, so your IT teams can seamlessly manage telemetry and operational data across different storage, networks, and applications.

AIOps combines machine learning, big data, and analytics. It helps your IT and operational teams as a key part of your digital transformation initiatives.

Application performance monitoring (APM)

Modern applications use complex software technologies to run and scale across cloud environments. It is challenging to gather metrics using traditional methods in modern scenarios, such as data exchanges among components like microservices, APIs, and data stores.

Software teams can adopt AI for application performance monitoring to gather and compile relevant metrics at scale.

Read about application performance monitoring (APM) »

Root cause analysis

AI/ML technologies are effective at determining the root cause of an incident. These technologies can rapidly process large volumes of data and correlate multiple potential causes. By adopting AIOps, your organization can investigate beyond symptoms and alerts to the true causes of system performance issues.

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 detect data deviations and allow quick 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.

With AIOps, your organization takes a more proactive approach to resolving IT operational issues and optimizing performance. Instead of relying on sequential system alerts, your IT teams use machine learning and big-data analytics. This helps break down data silos, improve situational awareness, and automate contextualized incident responses.

Here are the 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, often 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, benefiting from enriched metrics and workflows. They can use AIOps analytics to monitor IT workloads on multicloud environments. IT and operational teams share information via a common dashboard to streamline diagnostic and assessment efforts.

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 analytics generated by ML algorithms. 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 code and confirm that problems are resolved before releasing software updates to affected customers.

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 to monitor the performance of networking, applications, and cloud computing.

Domain-agnostic AIOps solutions allow IT teams 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.

AIOps is a relatively new concept that uses 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 operations workflows. It helps organizations move software from development to production and share information between software and operations teams.

On the other hand, AIOps is an approach for using AI technologies to support existing IT operations processes outside of software development.

AIOps vs. MLOps

MLOps is a practice that helps teams develop and deploy ML models across the organization. It involves model selection and data preparation. It includes the process of training, evaluating, and deploying 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 maintain systems within a defined operational window. Instead of relying on manual approaches, SRE teams improve system reliability and, consequently, the customer experience by setting performance goals. This practice automates system operations and performs checks with software tools to meet those goals.

AIOps complements SRE by helping organizations meet their SRE goals.

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.

AIOps involves more complex data transformations, leveraging information from DataOps to detect, analyze, and resolve incidents.

Amazon Web Services (AWS) provides a range of AI services that help you get started with AIOps implementations. You can use these services to enhance customer experiences, improve business service delivery, and reduce costs.

Here are some AWS offerings to support your AIOps requirements:

• Amazon CloudWatch is an intelligent observability service that provides actionable insights across applications and infrastructure. CloudWatch offers fully-managed solutions enhanced with generative AI or integrated through OpenTelemetry with open-source compatibility

• Amazon Managed Grafana provides scalable, secure data visualization for your operational metrics, logs, and traces.

Get started with implementing AIOps on AWS by creating a free account today.