Artificial Intelligence for IT Operations (AIOps) automates IT processes — including anomaly detection, event correlation, ingestion, and processing of operational data — by leveraging big data and machine learning.
With AIOps, teams can significantly reduce the time and effort required to detect, understand, investigate, and resolve incidents at scale. Being able to save troubleshooting time allows IT teams to focus on higher-value tasks and projects.
AIOps consolidates monitoring and adds machine learning and statistical analysis to identify threats and remedy problems in real time. It typically uses a scalable data platform to bring together all types of IT data. This can include:
- Historical data
- Logs and metrics
- Performance and event data
- Infrastructure and network data
- Incident-related data
- Application data, such as traces
With all of this data centralized, AIOps tools apply advanced analytics and machine learning to accurately and proactively identify issues that need attention. These tools are necessary to analyze the sheer amount of raw observability data generated by modern organizations. This data is often complex as applications, workloads, and deployments continue to be distributed and dispersed across the cloud (hybrid or multi-cloud).
AIOps platforms help manage the complexity and fast rate of change that characterize modern environments. These tools can help IT teams:
- Identify significant alerts: Not all events are created equal. AIOps can separate signals (abnormalities) from noise (everything else going on).
- Enable root cause analysis: AIOps tools can identify symptoms of a larger problem, surface correlated factors, and suggest solutions to resolve the issue.
- Monitor in real time: At a foundational level, AIOps tools can monitor a number of different systems for anomalies. Then the right teams can be notified when an issue occurs. This can be taken a step further with auto-remediation, the ability to allow alerts to trigger system responses. With auto-remediation, issues can be resolved before end-users are aware they happened.
- Continuously improve: Like anything that leverages machine learning, it gets better over time. As issues are identified and resolved, models can learn and adapt, helping them better tackle future problems.
In order to get the most out of your tool investment, AIOps solutions need the right capabilities. This includes:
- Integrations: In order for an AIOps tool to be effective, it needs to have comprehensive integrations into the tools and systems you already use. This can help you ingest data from a wide range of sources to identify what is working and what is not within your organization.
- Mapping and tracing: Being able to view your infrastructure, processes, transaction flows, and dependencies with intuitive visualizations allow teams to get a better idea of what is happening from a bird's eye view. As such, teams need service dependency mapping capabilities and distributed tracing to support investigations into telemetry data.
- Platform approach: Leveraging a unified platform for AIOps that supports observability, APM, and more, can give you a single view into your data, breaking down traditional silos.
- Support for cloud-native technologies: AIOps tools need to be able to aggregate data from containers, microservices and orchestration tools, such as Kubernetes. This helps AIOps tools learn what is happening on both an application and infrastructure level, helping support DevOps workflows and scalability.
AIOps is important because it can help IT operations spend less time troubleshooting. Their time can be better spent envisioning and implementing their goals. By leveraging AI and machine learning, AIOps can help:
Aggregate multiple data sources
Many AIOps solutions can monitor log files, configuration data, metrics, events, and alerts. This includes any unstructured data types that are particular to your organization. They can pull them into one place, creating a "single pane of glass" for an organization. Once centralized, the data can be reviewed much more efficiently.
Investigate the root causes of problems
One of the key benefits of AIOps is root cause analysis. AIOps can help teams find the origin of any issues that arise across systems. Once a problem is identified, IT teams can go straight to the source and correct it.
Forecast potential problematic scenarios
AIOps may use predictive analytics and machine learning to catch anomalies that your IT team might not notice and even forecast future trends. AIOps anomaly detection algorithms compare real-time and historical data from different sources to look for unusual, problematic patterns. They can catch red flags that might not set off a high-priority alert but could still cause significant issues down the line. In some cases, AIOps can resolve data issues entirely on its own with automatic remediation. No human intervention needed.
Spot and filter false alarms
Event correlation with AIOps can pinpoint and filter events that are “white noise.” These white noise events may set off an alarm but aren’t actually important issues. The system then sets them aside as low-priority items. This automatic organization lets your IT operations teams focus on the most important tasks first.
Continuously learns from data streams
An AIOps machine learning job improves upon itself as it analyzes all your data flows. As the ML models advance, they get better at identifying the anomalies your business faces. Supervised machine learning models take input from the user to more accurately understand your priorities over time. As your business evolves, so does AIOps, making itself even more helpful to your Ops team.
- Supports your workforce
Highly-skilled DevOps and operations teams can become overwhelmed by manual and tedious data analytic work. AIOps allows them to automate these tasks and offset parts of their workload. By delegating tedious analysis to the AIOps solution, they can focus their expertise where it is more critically needed.
- Accelerates development of new services and products
AIOps lets your business move faster. With the support of AI-based analytics , your teams can fast-track new IT services and features. By surfacing the most relevant information within an overwhelm of event and telemetry data AIOps also makes your incident management processes more efficient.
- Offers a broad view of the IT environment
AIOps solutions may leverage data lakes or data warehouses to efficiently store and aggregate disparate data streams within a centralized location. Cross-functional dashboards and analytics bring it all together so operations teams don’t have to divide their attention across multiple siloed views.
- Increases customer satisfaction
AIOps also monitors performance elements such as response times, usage, and availability. Predictive analytics help prevent incidents and outages, letting you resolve problems and roll out upgrades faster and better. As such AIOps helps you give your end user a seamless experience, reflecting well on you and your brand.
- Saves money
AIOps decreases Mean Time to Resolution (MTTR) and stops outages before they start. It can also offer insights into what workloads are driving costs within your organization. By fixing costly mistakes faster and using your teams more efficiently, AIOps gives you extra room in your budget.
AIOps and MLOps are complementary disciplines. DevOps is a set of practices and tools that may benefit from both.
AIOps vs. DevOps
DevOps represents a culture shift for organizations. It streamlines processes across development and operations to enable a more efficient software release and development lifecycle. Both AIOps and DevOps highlight the benefits of automation — removing time consuming manual tasks so teams can work smarter.
DevOps uses software to automate and integrate processes for software development and IT teams so they can work more efficiently. It streamlines development work by implementing Continuous Integration and Continuous Deployment (CI/CD).
AIOps incorporates AI and machine learning technologies to monitor and manage systems in order to resolve problems faster. This can complement DevOps processes by automating data analysis so the developers and Ops teams are not overwhelmed by the task of sorting through an avalanche of data. This helps teams avoid hours of manual analysis, make more informed decisions and proactively alerts team members to any issues.
Together, AIOps and DevOps enable teams to look at the entire system rather than being focused on specific tools and layers of infrastructure.
AIOps vs. MLOps
MLOps (Machine Learning Operations) is a complementary discipline to AIOps. Where AIOps employs machine learning to enable more efficient IT operations, MLOps is about standardizing the deployment of machine learning models. MLOps concerns itself with deploying, maintaining and monitoring the models in production. This may include incorporating feedback inputs for redeployment of improved models.
AIOps for financial services helps organizations automate data analysis and monitor at scale. For many financial institutions, AIOps solutions represent a security net when moving traditional on-premises systems into the cloud. These solutions can:
- Improve operational efficiency: Being able to understand problems holistically removes the burden on teams to sort through multiple systems manually.
- Meet and exceed customer expectations: In the financial industry, online customer experiences are a key strategic priority. With AIOps, organizations can ensure that customers get the real-time access they need by resolving incidents quickly.
- Data governance: AIOps solutions can help identify and document data sources, providing a necessary trail for governance.
- Lower costs: AIOps can automate many of the repetitive tasks a support team might handle now, for example, login issues or forgetting a password. This frees up time for IT teams, allowing them to tackle bigger challenges.
Financial Services Customer Spotlight: PSCU
PSCU used Elastic to substantially increase the number of data sources it could ingest. AIOps allowed them to improve their response to call center delays and potential customer-facing impacts like natural disasters.
Today’s digitally savvy retail customers are looking for seamless user experience. AIOps can help retailers delight customers by detecting and resolving issues proactively. With AIOps, retailers can improve operational efficiency and automatically respond to common problems before they affect customers. Resolving issues before they are a larger concern, contributes to revenue growth and improves customer loyalty.
Organizations can also analyze historical data to forecast future trends, helping teams make decisions around what products and services to offer. Having a centralized system gives teams visibility into their rapidly-changing global inventory to better anticipate when products need to be removed from a website.
Retail Customer Spotlight: The Home Depot When Home Depot faced a series of network interruptions, Elastic repaired itself before the load balancer servers even realized it. The home improvement giant’s senior IT Architect/Manager notes that Elastic "handles server loss so gracefully."
Elastic Observability is an AIOps solution that delivers full-stack visibility into complex, cloud-native environments. Elastic has been recognized as a Strong Performer in The Forrester Wave™: Artificial Intelligence for IT Operations (AIOps) in Q4 2022.
Elastic Observability can:
- Monitor logs to centralize and search through petabytes of logs, easily
- Use application performance monitoring (APM) to accelerate development and improve code quality
- Simplify infrastructure monitoring at scale
- Measure and track user interaction and performance
- Proactively monitor and verify the customer experience
Learn how you can use Elastic Observability to leverage AIOps for your organization