What is the difference between AI, ML, and AIOps?
AI, or Artificial Intelligence, is a broad term that is used to refer to all technologies that allow computational systems to perform tasks that are generally associated with human intelligence.
Machine learning (ML) is a subset technology of AI that runs data through advanced algorithms to alter or refine the behaviour of a system. In other words, ML analyzes data to learn from it and to make predictions or decisions without being explicitly programmed to do so.
AIOps is an application of AI and ML technologies in IT operations management. At its foundation, AIOps relies on monitoring platforms to ingest large volumes of high-variety data at high velocities. This data can be in the form of:
- Logs: Unstructured text from servers and apps
- Metrics: Time-series data (CPU, RAM, latency) from monitored IT systems
- Traces: Distributed maps of how a request travels through microservices
- Events: Discrete alerts from existing monitoring tools
Are AIOps and MLOps the same?
No, while AIOps refers to the integration of AI and ML technologies in ITOps (IT Operations). MLOps refers to a set of practices that is used to reliably develop and maintain AI systems. AIOps caters to IT admins, SREs and network technicians, while MLOps caters to data scientists, DevOps teams, and AI engineers.
What makes ITOps AI-driven?
IT operations management aims to provide smooth IT performance by ensuring quick fault-identification, resolution, and optimal IT performance. AI enhances this process by:
- Aggregating and analyzing IT data faster
- Setting baselines for IT performance and flagging anomalies
- Highlighting unseen impacts and root-causes
- Forecasting predictive trends and behaviour based on historical data
- Presenting monitored IT data into actionable insights
What are AIOps tools?
AIOps tools or platforms incorporate artificial intelligence and machine learning as a fundamental part of their operational workflow. Like most IT tools, AIOps tools are either installed on-premise, or deployed in the cloud. Since 2020, a significant share of market players have adopted AIOps features into their IT solutions.
How do IT tools incorporate AIOps features?
Acquisition of existing AIOps solutions: Many market players have enhanced their feature offering with AIOps capabilities by acquiring AIOps products and integrating them into their existing tool-sets. New Relic was an early adopter of this, when they acquired SignifAI in 2019 . We also saw this recently with Cisco's acquisition of Splunk .
Developing native AIOps capabilities: Other market players opt for the long game, developing in-built AI and ML engines that are incorporated into their IT solutions. These native engines are typically more deeply integrated into the tool's core architecture, leading to better performance and more reliable predictions. ManageEngine is a prime example of this, using their proprietary ML engine Zia to deliver AIOps features across their IT solutions like OpManager and Site24x7.
What are the features of AIOps tools?
AIOps incorporates a set of features designed to convert large volumes of IT data into high-quality insights. This generally involves the following set of features:
- A robust network data polling system to collect data from a variety of sources at high-velocity
- An ML-engine to aggregate, analyze, and predict data models
- An interface to present insights visually and contextually (Dashboards, alarms, reports, maps, and graphs)
- A degree of automation to ease manual effort
- A robust feedback system that compares predicted data with actual data to improve performance.
What are the benefits of AIOps in monitoring?
Monitoring tools are employed by IT teams as an early-warning system to flag outages and abnormal events. Most network and IT monitoring tools poll applications, network devices, and services at regular intervals to generate alarms. This has proven insufficient given the increasing complexity of modern networks.
AI simplifies network monitoring with the following advantages:
- Lesser MTTD (Mean-time-to-detect): AI can parse through data and flag abnormal events much faster than human operators
- Minimal alarm floods: AI can refine monitored data to reduce unnecessary alarm noise, improving monitoring accuracy
- Minimal false positives: AI can adapt your monitoring to dynamic network activity, ensuring that you detect actual issues alone.
- Root-cause analysis: AI helps human operators to track down the origin of network faults faster
- Faster MTTR (Mean-time-to-resolve): Faster MTTD combined with root-cause analysis and automation helps IT teams to restore services and optimize performance faster
How does AIOps help IT teams?
AIOps will benefit different IT teams in different ways. If your network or IT monitoring solution has AIOps incorporated in it, it would help your IT teams in the following ways :
IT admins: AIOps will automate many routine monitoring, alerting, and initial troubleshooting tasks, freeing up IT generalists to focus on strategic planning, complex problem-solving, and vendor management. They'll shift from reactive firefighting to proactive optimization. AIOps tools can help IT admins in the following manner:
- Set dynamically varying baselines for normal network performance and alert you when they are violated: Or- set and update dynamically varying alarm thresholds.
- Create a model of expected network performance: Forecast the future values of monitored performance metrics by analyzing the past data.
- Analyze resource consumption in mission critical devices and calculate the number of days left till they run out.
- Automate incident response for various IT scenarios with minimal manual effort on your part.
- Leverage dashboards, reports, maps, alarms, and suggestions to provide quick, actionable, AI-driven insights.
DevOps Teams : AIOps integrates seamlessly into CI/CD pipelines, providing intelligent feedback on deployments, predicting potential issues, and automating rollbacks if necessary. This enhances release velocity and stability, allowing DevOps engineers to focus on feature development and improving the delivery pipeline.
SRE Teams : AIOps empowers SREs with data-driven insights for proactive reliability management. It automates toil, enhances anomaly detection, speeds up root cause analysis, and enables predictive maintenance, allowing SREs to concentrate on architectural improvements and ensuring system resilience at scale.
How can I measure the results of AIOps?
While the high adoption of AI and AIOps technologies across different verticals and markets are definitely a positive sign for the effectiveness of AI, it's still imperative that we look at the results, particularly, the values that it provides for businesses.
What are the results that we should focus on?
When it comes to ITOps, there are diverse metrics that illustrate the effects of AIOps.
Mean-time-to-repair (MTTR): Case studies, research, and analysis indicate that AIOps technologies reduce the MTTR for IT issues by a significant margin. Numbers vary between the size and complexity of the monitored IT infrastructure and the industry verticals.
Quantity & quality of alarms: The effectiveness of AIOps solutions can be measured using the type of alarms generated by them. Non-AIOps enabled monitoring tools flag violations indiscriminately, resulting in a large number of 'low quality alarms'. Low quality here refers to the lack of context provided.
Technicians managing low quality alarms need to further: investigate, correlate, and deduce their own conclusions from multiple alarms. While AIOps tools reduce the number of alarms generated and increase the context for each alarm (Low quantity, high quality). AIOps also reduce the quantity of alarms generated by predicting potential issues: This means that the faults are prevented from ever occurring, further reducing alarm count.
User satisfaction: AIOps features have noticeable impact in improving user experience for IT services. Faster resolution times and less-frequent outages contribute to increased trust and better retention. Which directly contributes to the business goals set by the organization.
Team morale and productivity: IT teams that had to deal with time and effort-intensive, and repetitive tasks can now move on to more strategic and rewarding tasks. Organizations with effective AIOps solutions will most-likely have happier and more relaxed IT teams.
"[OpManager] meant that my work-life balance was possible... I could get real-time, proactive data anytime, anywhere" ~ Testimonial from Rohan Manuel, Project Manager at Work Healthy Australia, talking about how ManageEngine OpManager transformed his team's approach to IT management.
Other measurable impacts of AIOps are more specific and vary between organization to organization and industry to industry. For instance, organizations with a strong emphasis on DevOps processes would witness Faster time to the market and improved collaboration between DevOps and SRE teams. Similarly, the success of AIOps implementations can be measured by metrics like- reduction in critical security incidents, improvements in the performance and resilience of mission-critical services, difference in user incident reporting and automatic detection, etc.
How should I choose an AIOps solution?
As always, this depends on a multitude of factors and can vary from organization to organization. Nevertheless, we can outline some general factors that you might want to look at before choosing a monitoring vendor for their AIOps capabilities.
Is it really AIOps? Or is it just fancy terminology?
Many vendors position themselves in the AIOps market by claiming to have similar features. Usually this involves using terminologies that are associated with AIOps without actually claiming that they are AI/ML powered.
Are the features home-grown or acquired?
Naturally, features that are developed in-house integrate better with the existing functionalities of IT tools than tools that are acquired and shoe-horned into the ecosystem. While acquisitions are now part and parcel of the market and a large number of vendors successfully pull off product acquisitions and integrations, it is still safer to go with vendors who have home-grown AIOps feature-sets.
What can it do for me? Do I need it?
This is important as many vendors can lock you in with fancy AIOps features with expensive billing that you may not necessarily need. It's better to focus on actionable results like- "How can it simplify my workflows?", "How does it help my IT team make better decisions?", or "Is it more efficient than my existing processes?".
How much do I have to pay for AIOps?
Cost is always a strong determining factor when it comes to IT decisions. The same applies for AIOps. Vendors might charge additional licenses for AIOps tools, or might bill you based on set usage rates. But at the end of the day, AIOps is just another feature. You should analyze it the same way you might analyze other features in your IT tools: Is it worth it?
How is ManageEngine's AIOps developed?
ManageEngine, and its parent company Zoho corporation, develops AI solutions at ZLabs. ZLabs is our research and development department that serves as a central innovation hub for all cutting edge tech at Zoho and ManageEngine. ManageEngine FSO's AIOps capabilities are powered by Zoho's Intelligent Assistant (Zia). Zia is ZLab's flagship AI solution and powers multiple tools and solutions across ManageEngine and Zoho.
Zia is focused on four aspects:
- Language processing
- Machine learning
- Hardware acceleration
- Database research
What are the AIOps features in OpManager?
OpManager comes with diverse AIOps functionalities that can help you enhance different stages of your IT operations.
Adaptive thresholds
Utilizing machine learning, OpManager automatically calculates and adjusts performance thresholds for network devices based on their historical data and typical usage patterns every hour. It eliminates the need for manual threshold configuration and adapts to changing network behaviour, reducing alert noise from expected fluctuations.
Performance trend forecasting
OpManager uses predictive algorithms to analyze historical performance data of network devices and interfaces, forecasting future trends in metrics like CPU, memory, and bandwidth utilization. It helps predict when resources are likely to become constrained, aiding in proactive capacity planning.
Forecast reports
Based on performance trend forecasting, this feature generates reports that predict when specific network resources, such as disk space on servers or memory on routers, are likely to be exhausted. These reports provide a clear overview of potential future resource constraints across the network.
Automated workflows
OpManager's drag-and-drop workflow feature allows IT teams to execute sequential, outcome-based, code-free workflows with 70+ unique actions. Workflows are flexible and can be triggered with alarms or a time-based schedule. Workflows can automate basic troubleshooting, diagnostics, and incident response with ease
Forecast Alerts
OpManager triggers proactive alerts when the predictive forecasting indicates that a network resource is expected to be exhausted within a specified timeframe. These alerts provide warning of potential resource crunches before they actually occur.
Zia insights and dashboards
Get quick, executable insights from monitored data with OpManager's in-built AI engine, Zia. Zia elucidates historical monitored data with insights like trend patterns, anomalies, percentage changes, maximum and minimum values, etc.
Zia's AI-powered dashboard provides a centralized view of intelligent insights and recommendations derived from the analysis of network monitoring data. It highlights potential issues, suggests root causes, and offers actionable advice to optimize network performance and prevent future problems.
The following features are in beta stage testing and will be released for wider use soon.
MCP-powered Gen-AI integration
The Model Context Protocol (MCP) server enables Generative AI LLMs to securely interact with OpManager's real-time IT data. With this integration, your IT team can use natural language to integrate data, identify issues, and drive incident response across incompatible IT platforms without manual correlation or custom integrations.
Smart event correlation
Smart event correlation cuts down alarm noise and speeds up root cause analysis with dependency-tracking, context-aware alarm clustering, and ML-powered pattern identification and prediction. With smart correlation, OpManager can convert chaotic alarm storms into meaningful, high-level problems that helps IT teams identify and resolve issues faster.
What sets OpManager apart from other AIOps solutions?
Our in-built AI engine, Zia
OpManager uses ManageEngine's native AI engine Zia to deliver end-to-end AIOps functionalities. Zia is incorporated in OpManager at a module level. Because of this, Zia can perform diverse functionalities seamlessly without affecting OpManager's speed or performance. You can perform the following AIOps functionalities with Zia and OpManager.
AIOps for every team
A key differentiator for OpManager is our commitment to making AIOps accessible to every team, regardless of budget. While most other tools in the market treat AI as a luxury add-on with separate licensing costs, we believe intelligent operations should be a standard. OpManager incorporates its AIOps features directly into the core product without charging extra fees. This approach ensures that you can leverage advanced ML-driven insights and automation across your entire IT department without worrying about the "AI tax" common in modern observability solutions.
Data security and privacy
At ManageEngine, we prioritize your data security and privacy by offering a deployment model that keeps you in total control. Unlike many cloud-only AIOps solutions that require you to ship sensitive telemetry to external servers, OpManager is designed to be managed on-premise. This ensures your data remains within your own security perimeter, helping you meet strict global compliance standards. With our focused approach to data privacy, you can enjoy the benefits of AI-powered insights without compromising the integrity or location of your critical infrastructure data.
Focus on R&D: The future roadmap
What truly sets OpManager apart is ManageEngine’s relentless focus on R&D and our vision for the future of autonomous operations. Our roadmap is dedicated to evolving Zia into a more proactive participant in your IT strategy, with upcoming AI-powered features focused on generative diagnostic reports and deeper predictive forecasting. By constantly refining our native algorithms, we ensure that OpManager doesn't just keep pace with IT trends but anticipates them, providing your team with the cutting-edge tools needed to handle the infrastructure challenges of tomorrow.
By Visakh,
Product marketer, ManageEngine
