Nov 18, 20224 min read

Introduction to AIOps. Difference between AIOps and MLOps

AIOps - Artificial Intelligence for IT Operation

AIOps combines big data and machine learning to automate IT operations processes, including event correlation, anomaly detection, and causality determination.

What is AIOps:

AIOps is a term used for Big Data analysis, Machine Learning, and Artificial Intelligence technologies to automate the identification and resolution of IT issue. It uses the data to monitor assets and gain visibility into dependencies of IT systems.

AIOps tools utilize machine learning to automate the management of applications. Some use tools to reduce the workload on developers and increase the response time of issues such as anomaly detection and dependency management.

Some of the AIOps tools are:

1. Splunk

2. Moogsoft

3. Datadog

4. Instana

5. Dynatrace

6. AppDymanics

7. BugPanda

8. PagerDuty

How AIOps works?

AIOps relies on the algorithmic analysis of IT data to help DevOps and IT operations teams to work faster and smarter. AIOps helps these teams detect and react to digital issues early enough to prevent an impact on customers and business operations. Modern IT environments generate complex and large quantities of data.

The AlOps platform ingests heterogeneous data from many different sources about all components of the IT environment - Networks, Applications, Infrastructure, Cloud Instances, Storage, and more!

Components of AIOps

The basic components and features of an AIOps software management tool can be summarized as follows:

Data aggregation One of the core capabilities of AIOps software is that it aggregates data from a variety of sources within DevOps infrastructure, including event logs, system tracing, apps, job data, tickets, and more. Removing data silos makes it easier to maintain oversight of IT infrastructure and correlate events on the network to determine their root cause.

Real-time processing Real-time data processing allows for a balance to be struck between ITOps meeting performance optimization requirements and security analysts managing countermeasures. With artificial intelligence, enterprise IT organizations can effectively ingest and analyze large volumes of data at scale and in real-time. As a result, these organizations can identify anomalies and respond more quickly to security events that are picked up by their AIOps tool.

Rule and patterns Artificial intelligence tools use rule application and pattern recognition algorithms to detect network events that warrant a response. They may even use machine learning algorithms that allow them to develop their own rules for detecting network anomalies based on training data sets. Rules and patterns are used to distinguish between network activity that is considered "normal" and that which is deemed "anomalous” to accelerate decision-making.

Domain algorithms Domain algorithms are specific to an industry or IT environment, and their contents and structure are dictated by an IT organization's unique goals and data. These algorithms define the specific operational goals that will be prioritized by artificial intelligence.

Artificial intelligence and machine learning capability The defining feature of AIOps. When it comes to AIOps technology, artificial intelligence implementations are geared towards "intelligent analysis" of large volumes of data and the capability of in-depth analysis via mathematical models that correlate and parse through machine data to produce histograms, charts, and visualization.

Automation Reducing workload for IT operators is one of the main reasons that AIOps tools exist, making automation one of their most important features. AIOps can be used to orchestrate and automate real-time testing of new software features and user stories or to perform in-depth log analysis and detect errors and anomalies

Uses of AIOps:

1. Anomaly detection

AIOps tools can scan large datasets and discover atypical data points. These outliers act as signals that identify and predict problematic events, such as data breaches, allowing businesses to avoid costly consequences, such as regulatory fines, negative PR, and declines in consumer confidence.

2. Automated remediation

AIOps helps automate remediation for known issues. Once the problems are identified, based on historical data from past issues, AIOps suggests the best approach to accelerate remediation.

3. Root cause analysis

With AIOps, a problem’s root cause can be determined, and appropriate measures can be taken to solve it. By identifying the cause of the issue, the team can avoid unnecessary work involved in treating the problem’s symptoms rather than the core problem. For example, AIOps platforms can track the cause of network outages, fix them immediately, and take protective measures to prevent similar issues in the future.

4. Intelligent alerting

AIOps filters and correlates meaningful data into incidents preventing alert storms from domino effects- for example, a failure in one system triggers an alert, impacting another system which also triggers an alert

5. Performance monitoring

AIOps acts as a monitoring tool for cloud infrastructure and storage systems. It reports on metrics such as usage, availability, and response times. It also uses event correlation to aggregate information, leading to better information consumption for users.

Difference between AIOps and MLOps

AIOps	MLOps
It is the practical application of Artificial Intelligence to augment, support, and automate IT processes.	It is a set of practices for better communication and collaboration between data scientists and operations professionals.
It combines big data and machine learning to automate IT operations.	This discipline combines machine learning, data engineering, and DevOps to uncover faster and more effective ways to deploy machine learning models.
AIOps systems identify the root causes of IT incidents, detect anomalies, and provide high-quality solutions that enable the tech teams to work towards a resolution.	Through dataset validation, application monitoring, reproducibility, and experiment tracking, MLOps makes it possible to efficiently get models into production and ensure they continue functioning reliably.
Automates root cause analysis and resolution	Increase efficiency and productivity of the team
It leverages revolutionary AI technologies to solve IT challenges	It is a crucial part of deploying AI and Data Science at scale and in a repeatable manner

Advantages of AIOps:

Reduce downtime. Application and system downtime can be costly in terms of lost revenue, lower productivity, and damage to your organization’s reputation.
Improve operational confidence.
Continually manage vulnerability risks.
Optimize skills and resources.
Focus on innovation.