- Terminology (A to D)
- AI Capability Control
- Asset Performance
- Bayes Theorem
- Big Data
- Chatbot: A Beginner’s Guide
- Computational Thinking
- Computer Vision
- Confusion Matrix
- Convolutional Neural Networks
- Data Fabric
- Data Storytelling
- Data Science
- Data Warehousing
- Decision Tree
- Deep Learning
- Deep Reinforcement Learning
- Diffusion Models
- Digital Twin
- Dimensionality Reduction
- Terminology (E to K)
- Edge AI
- Emotion AI
- Ensemble Learning
- Ethical Hacking
- Explainable AI
- Federated Learning
- Generative AI
- Generative Adversarial Network
- Generative vs. Discriminative
- Gradient Boosting
- Gradient Descent
- Few-Shot Learning
- Image Classification
- IT Operations (ITOps)
- Incident Automation
- Influence Engineering
- K-Means Clustering
- K-Nearest Neighbors
- Terminology (L to Q)
- Terminology (R to Z)
Table Of Contents
AIOps is a short form for Artificial Intelligence for IT Operations, a term coined in 2017 by Gartner. AIOps refers to using big data, advanced analytics capabilities, and machine learning to enhance the operational and functional workflows of IT teams. These platforms run on multi-layered technology and enable the simultaneous usage of several data sources and analytical tools.
The application environments in large-scale enterprise companies produce massive amounts of data and logging information. This ever-increasing complexity of incoming data and the hybrid nature of services and applications places considerable strain on IT operations. Subsequently, more companies are now employing AIOps than ever. The goal is to automate IT operations, intelligently identify patterns, augment common processes and tasks and resolve IT issues. AIOps brings together service management, performance management and automation to realize continuous insights and improvement.
AIOps solutions allow a centralized system of interaction between different IT functions to optimize operations. They have a standardized approach that is similar to human cognitive function. Listed below is the step-by-step process of implementing AIOps:
- Comb through huge volumes of data in a modern IT environment and select only the relevant information through some predetermined filtering and prioritization techniques.
- Conduct a thorough correlation analysis of the data to discover inherent patterns, dependencies, and relations within the data by intelligently reducing noise from it.
- Aggregate the data into different clusters and groupings to prepare it for advanced analytics.
- Investigate the root causes of different trends and events and learn the focal points of the operational information for inference purposes.
- Facilitate collaboration between cross-functional IT teams and escalate notifications to the relevant operators in case of certain events or issues.
- Automate resolution and remediation without needing any human intervention.
Key Capabilities of AIOps
Some of the key capabilities are as follows:
Noise, i.e. alarms and alerts, plague IT teams on an hourly basis. AIOps intelligently reduces noise by identifying root problems and giving solutions at high velocity. This, in turn, lowers the mean time to respond and repair (MTTR).
AIOps explores the underlying data to find important patterns and relations using correlation analysis. It uses factors like time, topology, and text of the data logs. It analyzes and processes incident alerts and extracts crucial insights from them which can help in identifying future incidents.
AIOps platforms streamline frictionless coordination between ITOps, DevOps, Security, SRE, and governance teams. It provides appropriate analytics and monitoring data to each function to accelerate cross-team collaboration within the company.
These solutions automate routine protocols such as processing minor system alerts, fulfilling user requests, or allocating IT resources to teams. They are also capable of automated incident responses and rectifications. This accelerates IT operations and enables quicker and more effective workflow sharing.
Remediation and Resolution
By conducting powerful root cause analysis, AIOps is able to troubleshoot problems at scale and automate solutions for recurring anomalous incidents and behaviors.
Use Cases of AIOps
AIOps systems leverage big data, predictive modeling, and advanced analytics to counter some popular use cases such as:
Proactive Anomaly Detection
Through analysis of historical big data, AIOps identify anomalous data points. This allows IT teams to recognize deviations from normal behaviors easily and prevent costly problems like data breaches or architectural breakages.
Root Cause Analysis
AIOps assist in accurately diagnosing the root causes of problems and remediating them with adequate solutions. This can help IT teams, by relieving them of the workload of tracing the core symptoms of these problems. AIOps platforms also set up safety protocols to shield against future issues.
AIOps is also used as a tool to monitor the entire network infrastructure. It monitors the health and performance of every component; broadcasting factors like availability, response times, and usability.
Apart from detecting operational issues early, it also employs advanced machine learning models to make predictions about potential future problems.
In cases where companies adopt a hybrid cloud model, AIOps gives excellent visibility into the interdependencies and increase operational efficiency. It also helps in taming cloud sprawl (uncontrolled cloud instances), thus preventing unnecessary overheads.
Benefits of AIOps
The benefits to businesses are unbounded, and they range from improvement in employee productivity to a direct reduction in functional costs. Other advantages that AIOps solutions offer to organizations are:
- Improved availability and reliability of IT systems
- Better technical collaboration between different IT functions
- Time-sensitive resolution and predictive management of potential issues
- Faster digital transformation by helping with cloud migration and security
- Aggregation of monitoring functions in an interactive, centralized system
- Reduction in false alarms for different types of events and alerts
- Faster development of services and better alignment in understanding their impact
Getting Started with AIOps
For the adoption of AIOps across the corporate, an organization needs to identify pain points in its IT operations that need to be improved upon. This will help finalize a business case for which AIOps will be implemented. It is imperative to understand the different types of AIOps solutions available to select the optimal one for the business. Domain-centric solutions only work in some use cases because they are specifically developed for a single domain. On the other hand, domain-agnostic solutions are able to function across different domains. Once the preferred solution has been selected, it is important to formulate a rollout and governance plan.
If you want to learn more about AIOps and other AI technologies, check out the relevant blogs at unite.ai to expand your knowledge of this domain.
You may like
AI in 2024: Major Developments & Innovations
Sovereign AI: Nations Investing Billions in Homegrown AI
Breaking Down the O’Reilly 2024 Tech Trends Report
Revolutionizing 3D Printing: Generative AI’s Role in Sustainable Design
Social Impact of Generative AI: Benefits and Threats
The UK Supreme Court’s Landmark Ruling on AI and Patent Law