The Architecture of AIOps: 4 Best Practices

Artificial intelligence for IT operations (AIOps), although the new kid on the DevOps block, is here to stay. It’s the future of DevOps and the future is here with us now. AIOps is just the infusion of artificial intelligence to the practice of DevOps. This practice has shown that it’s of great value to a lot of software development firms. Firms that have already implemented AIOps have recorded a massive boost in overall IT productivity. The volume of data inflow in software development is rapidly increasing and to get meaningful and workable information from it, the data must be processed. This may include some cleaning, sorting, classifying, and the like.

What Is AIOps About?

Trying to process data within the provisions of DevOps always seem like a difficult task for DevOps team members. It takes a lot of time to achieve just one task, like data cleaning for example. There’s no doubt that delays negatively affect the overall productivity of software development firms, but this problem can be solved by implementing artificial intelligence in the DevOps framework.

AIOps have helped DevOps teams become more productive and solve difficult tasks in the shortest possible time by automating various processes. For example, bug detection is less time consuming because artificial intelligence can study the normal code environment and throw up alerts whenever it detects an anomaly. Indeed, artificial intelligence is a great benefit to DevOps practices.

AIOps have helped DevOps teams become more productive and solve difficult tasks in the shortest possible time by automating various processes.

Pillars of AIOps

Integrating artificial intelligence into DevOps hinges on two major pillars, without which AIOps wouldn’t exist:

  • Machine learning
  • Big data

We’ll break these down one after the other to explore what each entails.

Machine Learning

Machine learning is a pivotal aspect of artificial intelligence that focuses mainly on studying human behaviors and then replicating them on its own. It uses data and computer algorithms to imitates how humans learn and act, and in turn, improves on them. When deployed after it acquires the knowledge to solve a task, it usually puts out results with better accuracy than humans can.

More so, Machine learning an aspect of AIOps that can clean, sort, and classify  large volumes of data. All these processes culminate at a point where DevOps teams can make meaningful decisions from the data. Comparing this to the conventional DevOps procedures, where the team members have to take time to sort and classify all the data they’ve gathered before making inferences, integrating AIOps with DevOps is a game-changer.

Machine learning works by building models. First, engineers build and train the models with a set of data known as training data. The training data is a very large volume of data that represents the normal condition of the software environment. The model analyzes and stores it in its memory, showing how the environment should look. This is very helpful in anomaly detection because once the testing data is passed through the model, the model can see any deviation from the nominal state and trigger an alert. This lets DevOps teams know that there is an issue in the shortest time possible.

Contributions of Machine Learning to DevOps

The following are some ways that machine learning scales up the practice of DevOps:

  • Anomaly Detection & Correction: AIOps with machine learning tends to study how your system works by learning from input data and adapting to its flow. This eliminates bugs and abnormalities in codes written by developers while saving time. Think of the normal practice in which team members finish their tasks before checking for anomalies and resolving them. Then they go repeat the same steps to check for anomalies after each fix. It’s tedious and stifles productivity.
  • Data analysis: When the DevOps team passes a large volume of data through an ML model, the model helps clean, sort, and group them into classes for further decision making.
  • Continuous workflow: Even when DevOps team members are on break, work doesn’t stop. Machine learning allows work to continue and never shut down.
  • Prediction and implementation: Based on the trained model, ML can predict the possible outcome of tasks and as well implement certain decisions without human intervention.

Machine learning and big data are the pillars on which AIOps run.

Big Data

This is the second pillar of AIOps. For AIOps to benefit any software development firm, the firm must have big data. By big data, we mean a huge volume of data inflow. This is now a normal phenomenon in software development, due to the exponential increase in data produced by modern applications. Most of these data come from reviews and feedback from users. Big data has three main characteristics:

  • Volume: The amount of data the system handles should be extremely large. This may involve anything from petabytes or exabytes of data consisting of records of millions of people.
  • Velocity: The rate of data inflow must be high. This means that a very large amount comes in every second.
  • Variety: The data inflow can be either structured or unstructured, and it will be diverse. It can be text messages, images, or videos. It may or may not fit neatly into a database, and it will require cleaning and sorting to make it useful.

If data doesn’t satisfy the three V’s, they’re not considered big data.

Machine learning and big data are the pillars on which AIOps run.

aiops architecture

Best Practices in Implementing AIOps

Getting the most out AIOps requires that DevOps engineers implement the following best practices.

Proper Data Management

DevOps engineers must be aware of the fact that ill-managed data always gives rise to undesired output and affects the decisions made when using the output. For top-notch outcomes, DevOps engineers should ensure the gathered data is properly cleaned, sorted, and classified. Performing these actions makes the process of browsing through large datasets easier than you might expect.

Proper Data Security

The security of user data is something firms can’t play with, as data protection regulation agencies can impose fines. Thus, the DevOps team must ensure they properly safeguard data within its control to avoid data breaches. This is especially important during the process of transmitting data into AI-enabled analysis platforms.

Proper Task Hierarchy Assignment

This is another best practice engineers should observe when implementing AIOps. It gives team members the ability to easily have an overview of all tasks and decide which they should attend to first.

Bearing this in mind, DevOps engineers should break down large tasks into smaller tasks, and develop a kind of hierarchy of priority for these tasks into the ML model. ML then takes it from there and does the rest. For instance, ML notify developers when tasks are due, who should handling a task, and so on.

Proper Use of Available AI APIs

AIOps is an amalgam of whatever artificial intelligence can offer to improve the productivity of IT operations. Therefore, the DevOps team should be on the lookout for great AI-enabled APIs capable of improving whatever task they have to accomplish.

Conclusion

Thinking of scaling up the DevOps practice of your software development firm? AIOps is the perfect strategy. AIOps can give you the superpower to provide great service delivery in the shortest time possible.

Having gleaned knowledge about AIOps and how to get the most from implementing it, the next step is to get acquainted with the AIOps platform best able to help you integrate AI into your daily DevOps routine. Netreo is the platform for all your AIOps implementation.

This post was written by Ukpai Ugochi. Ukpai is a full stack JavaScript developer (MEVN), and she contributes to FOSS in her free time. She loves to share knowledge about her transition from marine engineering to software development to encourage people who love software development and don’t know where to begin. 

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