AIOps: What It Is and Why It Should Matter to You

What Is AIOps?

First of all, let’s provide you with an easy-to-understand definition of AIOps.

AIOps helps to optimize the performance of various IT operations in each step of the DevOps workflow.

Now, what does this mean? AIOps uses both artificial intelligence and machine learning to analyze data about the current IT processes in the DevOps workflow. Most importantly, AIOps tries to detect inefficient patterns in the development or deployment workflow. Furthermore, AIOps is capable of detecting error patterns proactively and helps to prevent them before they reach the client.

At its core, AIOps uses a variety of analytics to detect those patterns, leveraging big data gained from the different stages of the DevOps workflow. Therefore, it’s not uncommon that AIOps consumes application logs and data from monitoring systems for detecting patterns.

In summary, AIOps helps with finding new insights in an automated way to foster continuous improvement. In the end, continuous improvement is also one of the primary principles of the DevOps movement.

Why Do We Need AIOps?

AIOps is a movement that grew out of the occurrence of several problems. The number of repetitive DevOps tasks keeps growing, DevOps teams are seeing their work increase, and they have to deal with much larger amounts of data that are valuable for the business. AIOps helps DevOps teams to manage the growing need for DevOps services within an organization. Here are the three main reasons to adopt AIOps.

1. Monitoring Efforts Exceeding Human Capabilities

Application monitoring has grown into its own field and has been accepted as an industry best practice. Monitoring helps a DevOps engineer to assess the health of a service or application. With the rise of popularity for the microservices architecture, the DevOps team suddenly had to deal with a large number of smaller applications all generating logs and requiring monitoring capabilities. As a result, the DevOps team is monitoring dozens of applications.

Even though the DevOps team can automate monitoring, warnings are often thrown that need to be investigated. With this huge increase in services, it’s hard to investigate each and every warning. Besides that, the DevOps team often has to deal with false positives.

AIOps utilizes machine learning and artificial intelligence to learn to evaluate those warnings. By feeding AIOps with feedback, it becomes better and better at evaluating those warnings. At some point, AIOps can take over the task of evaluating warnings as it can better predict whether a particular warning can form a threat for the application or end user. Therefore, it can effectively filter out false positives and reduce the time spent on solving warnings that matter for the DevOps team.

To summarize, the goal of AIOps is to become better at evaluating warnings than humans through the process of machine learning. This reduces the time the DevOps team spends on analyzing those warnings drastically, so they can spend time on other tasks that require their attention.

2. Data Increases Exponentially

With the increase of user devices and IT services, data is increasing exponentially. IoT devices, applications, APIs, and users—they all generate data. It’s hard for the DevOps team to keep up with this increase in data. Managing and, especially, analyzing all of this data is impossible. Therefore, there’s a serious need for machine learning capabilities to analyze the data and find valuable insights. In other words, the amount of data is exceeding the human capabilities of the DevOps team.

Plutora provides your team with a software delivery management solution that streamlines your delivery processes to improve time to value. Plutora’s solution helps you with scaling DevOps processes across your organization by focusing on the individual DevOps processes such as planning, building, packaging, configuring, testing, and, most importantly, monitoring. Why is Plutora so helpful? They help you with unifying all of the generated data and providing this data in real time for further analysis. Find out more about Plutora’s offering!

3. Improved Visibility

This might be an obvious advantage of implementing AIOps; however, let’s explore why AIOps provides your team with improved visibility.

Every application that generates data can be considered a data silo. A data silo refers to a data-generating program that’s not integrated with other data sources. AIOps helps you with integrating all those data-generating programs to run analytics and detect recurring trends. Furthermore, you can create automated response mechanisms whenever certain patterns are detected.

For example, say your AIOps implementation detects the pattern of a memory leak in one of your applications. You can immediately send a notification to a DevOps engineer, or you can decide to restart this particular service to see if the problem can be solved in this way.

In other words, with AIOps, your organization receives improved visibility over all the data-generating applications and tools.

How Can You implement AIOps in Your Organization?

Now you’re probably curious about AIOps and wonder how you can implement it in your organization. Well, here are some actionable tips to consider when starting your AIOps journey.

• First of all, get a basic understanding of AIOps, artificial intelligence, and machine learning. Understanding is key for knowing where and how you can best apply AIOps to your current DevOps efforts.

• Find repetitive DevOps tasks that AIOps can automate. Common examples are log data analysis to reduce the noise or trying to detect patterns in the data your monitoring systems generate such as CPU or memory usage data.

• Start small with simple repetitive tasks you think can be replaced by AIOps. Get your feet wet to see its potential before scaling up your AIOps efforts. Perhaps your company isn’t ready for AIOps. Therefore, it’s better to start with a small project.

• Expose your AIOps system to as many types of data as possible. This will reduce data silos in your organization, and it’s excellent data to train your AIOps implementation.

• Set metrics so you can accurately measure the positive (or possibly negative) impact AIOps has on your organization. For example, measure the mean time to repair (MTTR) as an important metric to determine the impact of AIOps on your resolution time.

Final AIOps Words

In short, AIOps is an exciting field that will likely grow during the upcoming months and years. DevOps requires adapting to this increasing need for scalability. AIOps provides the exact answer to this need for scalability by automating repetitive tasks through the use of artificial intelligence and machine learning. Want to learn more about AIOps? Read this amazing road map article about AIOps by Seth Paskin.