AIOps explained for companies

We find ourselves in an IT environment that is increasingly diverse, dynamic, and difficult to monitor, as well as isolated teams and very high user expectations.

In this scenario, AIOps appears as an application that may represent the future of IT operations management. The increase in demand for this type of service is improving your business approach to digital transformation initiatives. We review all the details!

What is AIOps

AIOps, or artificial intelligence for IT operations, can be defined as the application of AI capabilities, such as natural language processing and ML models, to automate and streamline operational workflows.

They automate critical operational tasks, such as monitoring performance, scheduling workloads, and creating data backups.

By integrating multiple manual, standalone IT operations tools into a single intelligent, automated operations platform, AIOps enables IT operations teams to respond more quickly and proactively to slowdowns and outages, with end-to-end visibility and context.

AIOps Benefits

If you are thinking of modernizing your operational services and IT infrastructure, you can gain numerous benefits when it comes to incorporating, analyzing, and applying increasingly large volumes of data.

Let’s take a look at the most important ones:

Reduced operating costs

Big data can yield actionable information, enabling the maintenance of a lean team of data experts. Equipped with AIOps solutions, IT teams can solve operational problems accurately and avoid costly errors.

In addition, they allow teams to spend more time on critical tasks instead of common, repetitive tasks. This enables companies to manage costs in an increasingly complex IT infrastructure and meet customer demands.

Faster problem mitigation

This service provides event correlation capabilities. It analyzes data in real-time and determines patterns that could indicate system anomalies. With advanced analytics, teams can efficiently assess the root cause of a system problem and resolve it faster, maximizing service availability.

Enabling predictive service management

IAOps makes it possible to anticipate problems by analyzing historical data with Machine Learning technologies. These models analyze large volumes of data and detect patterns that people cannot recognize.

Instead of reacting to problems when they have already occurred, teams can use predictive analytics and real-time data processing to reduce disruptions to critical services.

Optimizing IT operations

AIOps provides a common framework for aggregating information from multiple data sources, making it easier for IT teams to collaborate and coordinate workflows without human intervention, significantly improving productivity.

Improved customer experience

These tools can analyze large amounts of information from different sources (such as chats, emails, or other channels), and can be used to analyze customer behavior and improve service delivery.

Service interruptions that affect customers can also be avoided, providing an optimal digital experience by ensuring constant service availability and an effective incident management policy.

Cloud support

IAOps establishes a unified strategy for managing public, private, or hybrid cloud infrastructures. Organizations can migrate workloads from traditional environments to a cloud infrastructure without worrying about complex data migration.

This improves observability, so teams can seamlessly manage data across different types of storage, networks, and applications.

AIOps Platform

As mentioned above, AIOps uses advanced analytics to automate and optimize IT operations processes and works by following these steps:

1. Data collection: they start by collecting information from various sources, such as application logs, event data, configuration data, incidents, performance metrics, network traffic, etc. These can be structured or unstructured.
2. Data analysis: This collected data is analyzed using ML algorithms and predictive analytics, to find anomalies that may require the attention of IT staff. This ensures that real problems are separated from noise or false alarms.
3. Inference and root cause analysis: a root cause analysis is performed to help locate the source of problems to internally prevent recurring outages by investigating the root causes of current problems.
4. Collaboration: once the root cause analysis is completed, AIOps notifies the relevant teams and individuals, providing them with relevant information and promoting efficient collaboration despite the possible geographical distance between them. This helps preserve event data that could be essential for identifying future problems of a similar nature.
5. Automated troubleshooting: problems can be solved automatically, significantly reducing manual intervention and speeding up incident response. This can be reflected in scaling resources, restarting a service, or running predefined scripts to address problems.

AIOps Use Cases

The most widespread examples of the use of AIOps are found in companies that also use DevOps or cloud computing, as well as in large companies or companies with complex processes.

As we discussed above, by providing them with additional information about their IT environment, they gain more visibility into production changes. Some examples of the most common use cases include:

• Elimination of hybrid cloud risks: Cloud platforms have complex architectures and interactions between different components, which can generate risks, such as loss of efficiency and accuracy. AIOps eliminates these risks by breaking down the operational limitations of the environment.
• Process automation: recognizing problems earlier and facilitating communication between teams helps companies with large and/or complicated IT environments.
• Anomaly detection: uses AI to scan large amounts of historical data and categorize patterns faster than human operators, allowing problems and their underlying causes to be identified quickly and accurately.
• Performance monitoring: AIOps bridges the gap of determining which underlying resources support specific modern applications, as they are often divided by numerous layers of abstraction. It does this by functioning as a monitoring tool for storage, virtualization, cloud infrastructure, and metrics reporting.
• Understanding customer needs: helps companies better understand their customers’ demands by collecting real-time interaction data and using it to provide a better customer experience. It can also modify products in response to customer feedback, as well as increase customer satisfaction levels.
• Threat detection: can help identify security risks, anomalies, and malicious activity patterns. By analyzing log data, network traffic, and security events in real-time, you can respond quickly to incidents, reducing threats and attacks.
• Capacity management: also helps companies assess usage trends and predict resource requirements to ensure optimal performance and reduce costs.

AIOps vs MLOps

As AI environments become more complex, traditional operations management tools are struggling to keep pace with the demands of ever-increasing data generation.

As a result, many companies are relying on advanced tools and strategies such as AIOps and MLOps to turn large amounts of data into actionable information that can improve decision-making and the bottom line.

As we have been discussing throughout the article, AIOps refers to the application of AI and ML techniques to improve and automate various aspects of IT operations. As such, it is designed to leverage data and knowledge generation capabilities to help organizations manage increasingly complex IT pipelines.

MLOps, meanwhile, is a set of practices that combines ML with traditional data engineering and DevOps to create an assembly line for building and running reliable, scalable, and efficient ML models. It helps enterprises optimize and automate the end-to-end ML lifecycle, model deployment, model orchestration, health monitoring, and data governance processes.

Thus, MLOps ensures that everyone involved in the process (from data scientists to software engineers to IT staff) can collaborate, monitor, and continuously improve the models to maximize their accuracy and performance.

Both AIOps and MLOps are business-critical, AI-based practices, but differ fundamentally in their purpose and level of specialization in artificial intelligence environments. While the former includes a variety of analytics and AI initiatives that aim to optimize IT operations; the latter specifically addresses the operational aspects of ML models, promoting efficient implementation, monitoring, and maintenance.

The main differences according to IBM are:

Scope and Approach

The AIOps methodology is aimed at improving and automating IT operations, optimizing and streamlining operations workflows by using AI to analyze and interpret large amounts of data from various systems. They leverage big data to facilitate predictive analytics, automate responses and information generation, and optimize the performance of enterprise IT environments.

On the other side, we find MLOps, which focuses on ML model lifecycle management and aims to bridge the gap between data science teams and operational teams so they can reliably and efficiently transition ML models.

Data characteristics and preprocessing

AIOps tools handle a variety of data sources and data types, but preprocessing is often a complicated process involving: advanced data cleansing procedures, transformation techniques to convert disparate data formats into a unified structure, and integration methods to combine data from different systems and applications to obtain a holistic view.

MLOps, on the other hand, focuses on structured and semi-structured data and uses preprocessing methods such as feature engineering to create meaningful input variables, normalization and scaling techniques, and data augmentation methods to improve training data sets.

Key Components

AIOps relies on Big Data-based analytics, ML algorithms, and other AI-driven techniques to continuously track and analyze ITOps data. This process includes activities such as anomaly detection, event correlation, predictive analytics, and NLP processing.

MLOps involves a series of steps that help ensure the implementation, reproducibility, scalability, and observability of ML models. This includes a variety of technologies (macros, data pipelines, CI/CD, Kubernetes, version control systems, etc.) that optimize the model lifecycle.

Development and deployment

AIOps integrates analytical and statistical models into existing IT systems to improve their functions and performance.

MLOps, on the other hand, prioritizes end-to-end management of ML models, using CI/CD channels to automate predictive maintenance and model deployment processes. It can then focus on updating and retraining models as new data becomes available.

Key users and stakeholders

The primary users of AIOps technologies are IT operations teams, network administrators, and DevOps and DataOps professionals, who benefit from improved visibility, proactive problem detection, and rapid incident resolution.

MLOps platforms have as their primary users data scientists, ML engineers, DevOps teams, and ITOps personnel who benefit from model automation and optimization, as well as rapid time-to-value from AI initiatives.

Tracking and feedback loops

AIOps solutions focus on monitoring KPIs across IT operations, incorporating user feedback to iterate and refine analytical models and services. This enables teams to quickly identify and resolve problems.

MLOps monitoring, meanwhile, requires teams to continuously track metrics such as data accuracy, precision, recall, and variance. Based on these metrics, MLOps technologies continuously update ML models to correct performance issues and incorporate changes in data patterns.

Use cases and benefits

AIOps helps companies increase operational efficiency and reduce costs by automating routine tasks that free workers to focus on more strategic AI initiatives. They also find and fix problems before they cause downtime or impact user experience.

MLOps technology helps accelerate time to market for ML models, increase cross-team collaboration, and scale AI initiatives across the organization. It also helps maintain compliance and data governance standards, and its use cases are very broad and scalable to different industries.

AIOps Solutions

The future of AIOps is very promising. According to a report by The Insight Partners, the global AIOps platform market is expected to grow from $4.9 billion in 2023 to $46.2 billion in 2031.

It is expected to help enterprises improve their IT operations by minimizing noise, facilitating collaboration, providing full visibility, and boosting the management of these services. It has the potential to accelerate digital transformation by offering companies a more agile, flexible, and secure infrastructure. In addition, it is expected to mature and gain market acceptance, resulting in widespread incorporation into DevOps initiatives to automate infrastructure operations.

At Plain Concepts we apply Machine Learning and predictive capabilities to IT operations and DevOps environments to achieve real-time observability, insights, and risk prevention.

We will help you understand and get the most out of technology in today’s complex IT environments, finding the balance between stability, speed, and agility. We take a proactive approach that, driven by AIOps, will enable you to manage the volume, variety, and velocity of data:

• Monitors activities in real time
• Detect and address issues before they impact the company
• Frees your IT staff to focus on higher-value, more complex tasks
• Improves productivity and boosts the ability to innovate
• Improve performance and user satisfaction

Don’t wait any longer and join the IAOps revolution now!