AIOps Overview

What do we mean by AIOps?

AIOps, an abbreviation for Artificial Intelligence for IT Operations, involves the application of Artificial Intelligence (AI), and in particular machine learning (ML) and big data analytics, to automate and enhance IT Operations. It help organisations to manage complex IT environments by via: the detection, diagnosis, and resolution of issues more efficiently than traditional methods.

Let's break the components down and add a bit more detail:

Automation:

AIOps platforms enable you to automate repetitive tasks, which can free up your IT staff, enabling them to focus on more strategic initiatives.

Data-driven insights:

Vast amounts of data from various IT systems can be leveraged to identify patterns, anomalies and potential problems.

Proactive monitoring:

Real-time monitoring and proactive identification and resolution of issues can be facilitated by AIOps, to isolate and eradicate these issues before they impact users.

Improved efficiency:

AIOps can be significantly improved by employing automated processes and providing data-driven insights.

Enhanced decision-making:

Using AIOps platforms and tools, IT teams can obtain the information they need to make more informed decisions about resource allocation and problem resolution.

Key components:

AIOps platforms will typically involve data collection and aggregation, real-time processing, rule-based analysis, utilising ML algorithms, and automation capabilities.

Benefits:

Deploying an AIOps platform can lead to reduced Mean Time to Recovery (MTTR), potentially leading to better overall IT performance, and improved service availability.

The Four Key Stages of AIOps

The four stage of AIOps include; data collection and curation, training models on your data, building automated solutions that respond to the predictions of the models, and deployment for anomaly detection.

Data collection: Because modern IT systems are so complex, it is critical to identify and collect useful data to ensure a successful AIOps deployment. The wrong data, or too little data, could create ineffective and inaccurate models. Data scientists and cross-functional teams can help to curate the right data to help build a more effective AIOps solution. AIOps facilitates the integration of siloed data across an infrastructure. This data can include historical systems data and events, logs, network data, and real-time operations.

Model training: What functionality do you want in your AIOps deployment? The objectives of your AIOps solution and the quality of your data will determine how models are selected and trained. Key areas to focus on include; security, performance, storage optimisation, and scalability. Models should also be designed to retrain themselves over time to stay accurate and effective, because IT environments change constantly.

Automation: Well-trained AIOps models work best when paired with automated tools and applications that can respond to insights in real time. Using these tools allows AIOps to instantly respond to predictive analytics and model outcomes, reducing tedious manual effort. It is possible to create these tools from existing observability tool set, or developed as custom applications which can be tailored to meet specific needs.

Anomaly detection: When your models have been deployed, real-time analytics can be utilised to speed up anomaly detection and response. It is also possible to incorporate data from previous outcomes into feedback loops to continuously help retrain models to improve accuracy and effectiveness over time.

Use cases for AIOps

There are four main ways that DevOps, Site Reliability Engineering (SRE), and on-call teams are putting AIOps to good use:

Identifying issues before they happen: The first step with issue detection is identifying potential problems in your software, before it impacts the customer experience. AIOps tools have the ability to automatically detect anomalies in your environment and trigger notifications to monitoring systems as well as other collaboration tools, such as Slack.

1. Reducing noise and connecting the dots: AIOps can be put to use, helping teams to prioritise and focus on critical issues, by correlating related alerts, events, and incidents, then enriching them with context from historical data, or other tools. Some of the more advanced tools utilise both machine-generated (i.e., time-base clustering, similarity algorithms, and other ML models) as well as decisions that are human-generated, which help to suppress noisy of low-priority alerts and identify meaningful patterns. AIOps tools can also classify incidents based on the four SRE 'golden signals' - latency, traffic, errors, and saturation, to provide valuable context. This can help to more easily diagnose the root cause of an issue and determine how to resolve it.
2. Incident data can be automatically routed by AIOps to individuals or teams best equipped to respond to them. This can help to reduce the number of noisy alerts sent to the wrong people who work in decentralised, distributed teams. This can also help to cut down the time it takes to route critical incident data to the right people. ML models are run for AIOps tools to help them evaluate data from your incident management and monitoring tools, and can provide suggestions regarding individuals or a team that could resolve a particular problem faster. This will be because they have either already seen something similar in the past, or are experts at the specific components that are failing.
3. Automated incident remediation: This is the last, and most critical step in resolving incidents, where the problem is actually fixed. This process is streamlined using AIOps tools by automating workflows and remediation tasks to resolve the incident when it occurs, and reduce mean-time-to-resolution (MTTR). Naturally, we are always looking to close the gap between detecting a problem, diagnosing it, and fixing it. These last-mile challenges can be addressed by expanding the scope of AIOps.

Selecting the right AIOps platform

The advanced capabilities available in today's AIOps solutions effectively extends observability functionality far beyond traditional IT operations platforms.

Building a strong foundation, utilising a rich set of observability tools, and automation which adapt to your organisation's unique needs, gives you the ability to deploy future-proof AIOps observability, diagnosis and issue resolution right from the get go.

Integrating your current incident management tools into an AIOps solution can give you the best of both worlds, utilising tools you already have in place, and introducing modern, best-of-breed AIOps tools into the fray. This allows you to tap into your current processes and fine tune them as you introduce more AIOps capabilities.

It is also important to choose an AIOps platform that can integrate with your existing incident management tools. This will ensure that your current investment is leveraged, whilst adding new, cutting-edge features and functionality to build and deploy an effective AIOps solution.

In summary, deploying an AIOps solution can introduce intelligence to your current incident management processes, ensuring faster detection, noise reduction, and can decrease false positives without requiring a complete overhaul of your DevOps/NetOps workflows.