For instance, with ITOps, teams were meant to handle incident responses through manual efforts, whereas with AIOps tools, automation has been introduced to deal with such strenuous tasks.
With the changing approach of AIOps, artificial intelligence aims at streamlining several IT operations with the use of ML algorithms and automation. Due to this, organizations can now transform their manual workflows into predictive and smooth processes.
In this article, you will get to learn more about what AIOps is and how it enables you to replace your traditional IT tasks with technology-backed confidence.
Brief Understanding of Artificial Intelligence for IT Operations (AIOps)
AIOps, or Artificial Intelligence for IT Operations, is defined as a technology-driven process that uses AI strategies or techniques to maintain an IT infrastructure. It enables you to automate major critical operations within the organization, such as:
- Workload scheduling
- Data backups
- Performance monitoring
- System failure diagnosis
- File logging
The AIOps tools make use of technologies such as NLP (Natural Language Processing) and modern ML (Machine Learning) to improve your organization’s operational efficiency. Using these tools, you can bring in personalized and real-time data to your IT operations through automated data collection and analysis from diverse sources.
Key Components of AIOps
AIOps can conduct your IT operations based on these key components, which include:
1. Data Ingestion:
It is the process that will help you collect all the critical data points from diverse applicable sources. This way, AIOps can help you with easy incident management and report-building efforts.
2. Machine Learning:
Data collected during the ingestion process will be fed to the trained ML algorithms, enabling them to recognize file logging patterns.
3. Pattern Recognition:
The AIOps system will assess operational patterns to identify potential trends, predict future vulnerabilities, detect anomalies, and point out the failure points. Eventually, you will be at the helm of securing your brand operations with accurate insights.
4. Automation:
Finally, the AIOps system will have all the variables to implement automated actions, based on the insights collected across all the above components.
Reasons Why AIOps Hold Importance Over ITOps
ITOps had its day in the past! However, AIOps brings in modernization to your operational services in terms of ingesting, analyzing, and applying large data volumes to your organizational tasks. Here are some reasons why they are important for you to adopt in this decade and beyond:
1. Reduce the Operational Expenses:
Implementing AIOps will enable your organization to collect actionable insights from large volumes of data. The data experts leverage AIOps to augment the IT team towards resolving all potential operational issues with utmost precision. Thus, costly errors can be prevented!
2. Lower the Problem Mitigation Time:
With AIOps over ITOps, you will have event correlation features, enabling you to analyze real-time data and specify patterns indicating system anomalies. By using advanced analytics by AIOps, your operations team can efficiently determine the root cause of potential issues and resolve them at the earliest.
3. Streamline & Optimize IT Operations:
When in a conventional ITOps setup, your teams often have to work with disparate sources of data. Due to this, your overall business operations will slow down and might be affected due to human errors. With AIOps in place, you get a common framework to aggregate information from diverse data sources. Thus, it will enable your IT teams to coordinate and collaborate on various workflows without the need for human intervention. Your operational productivity will increase immensely!
4. Leverage Predictive Service Management:
Using the predictive analytics features of AIOps, you can now anticipate any potential future issues and mitigate them beforehand. It is made possible by AI analyzing the historical business data using ML algorithms. Such ML models will assess large data volumes to identify patterns that might have been missed by human eyes. This way, you can reduce the rate of current or future disruptions, especially for the critical services.
5. Enable Cloud Migration:
Upgrading to AI Ops will help you implement a unified solution for managing the public, private, as well as hybrid cloud infrastructures. Beyond that, you can also migrate your workloads from a traditional IT setup to a complete cloud infrastructure. The AIOps tools also enable your IT team to easily manage critical data across different cloud platforms.
Working Process of AIOps
In order to implement AIOps into your IT infrastructure, you ought to know about how it works and what its underlying mechanisms are. Therefore, here is a brief elaboration on the complete step-wise working process of AIOps for your IT tasks:
1. Collection of Data:
The first step when working with AIOps is data collection from scattered sources, including cloud infrastructure, applications, servers, and more.
2. Data Correlation and Normalization:
Once all the data is collected, AIOps will automatically normalize it and structure or organize it in a single format. Furthermore, AI then correlates the structured data from diverse sources.
3. ML-Based Data Analysis:
All the AIOps systems use ML algorithms for analyzing the correlated data. This process helps the ML model spot any suspicious patterns and flag anything that it feels is unusual. Such discoveries become essential to detect & resolve performance issues.
4. Event Co-Relation:
In the second step, your AIOps system also correlates the data for creating specific events. Furthermore, it will then associate multiple events to create meaningful incidents. This entire process is referred to as incident management.
5. Root Cause Analysis:
Following that, the AIOps system will then conduct a root cause analysis to identify the underlying issues that might cause security or performance compromises. For instance, AIOps can help you identify if a database query flaw is affecting application latency.
6. Automated Response:
The best thing about AIOps is, it can fix the identified issues all by itself. After a thorough analysis of all the available data and identifying suspicious patterns, it will then trigger automated responses to resolve them. There will be no need for manual intervention from your IT team.
Use Cases of AIOps for IT Infrastructure Management and Task Optimization
Here are some of the use cases that express how AIOps can be beneficial for supporting your IT or operational teams to conduct seamless digital transformation activities:
1. APM (Application Performance Monitoring)
The modern applications use complex technologies to run across cloud environments. Therefore, it becomes challenging for your IT team to collect metrics using just the traditional methods. AIOps for APM can help streamline the process of gathering metrics on factors such as data exchanges between diverse components, such as storage, APIs, microservices, and others.
2. Anomaly Detection
AI technologies have the ability to help determine abnormal behaviors that affect the system operations. With AIOps, you can acquire real-time insights along with predictive potential to quickly identify data deviations and implement corrective responses. Moreover, AIOps will also reduce your team’s dependency on traditional system alerts while dealing with incidents and enable IT teams to implement rule-based remediation policies to automate certain actions.
3. Cloud Automation & Optimization
With AIOps solutions, you can extend support for cloud transformation with observability, workflow automation, and transparency. You can now deploy & manage cloud apps that require added agility and flexibility while managing the interdependencies. Most organizations today use AIOps solutions for provisioning and scaling compute resources whenever needed.
4. Support for App Development
The DevOps team makes use of AIOps tools for improving their code quality. Using the process, they can automate code review efforts and also implement best app development coding practices. Not just that, but AIOps can also help developers detect bugs or errors at the earliest, before the app is deployed. Instead of executing quality checks at the end of the SDLC, AIOps ensures they are done simultaneously across all stages.
Conclusion
The technology dominance around the world is evolving at a rapid pace, and it becomes important for businesses and respective industries to stay ahead of the future trends. AIOps is one of the integral solutions that has changed the way IT infrastructure deals with its operational attributes.
By leveraging the power of big data, intelligent automation, and machine learning, you can empower your IT teams to seamlessly deal with modern environments with confidence, agility, and speed.
Whether you want to detect anomalies in your business system or predict incidents before they adversely affect your operations, AIOps can redefine performance, efficiency, and reliability for you!
