AIOps and MLOps are so often confused with each other because they share some common characteristics. Even so, they belong to separate domains and are applied in different ways. AIOps and MLOps also have different goals. If you want to know what is MLOps and what is AIOps, read on to find out and understand their differences.

Defining AIOps

AIOps refers to either “Algorithmic IT Operations” or “Artificial Intelligence (AI) for IT Operations.” The term was coined by Gartner in 2016 as a category in machine learning (ML) that deals with the enhancement of IT operations through analytics technology.

According to Gartner, AIOps uses modern ML, big data, and several other analytics technologies to indirectly and directly enhance IT operations. These enhancements include service desk automation and monitoring with the goal of using personal, dynamic, and proactive insights.

AIOps platforms allow users to utilize several data collection methods, data sources, presentation technologies, and analytical technologies—including deep and real-time technologies.

Simply put, big data and ML are the main parts of AIOps. Their ultimate goal is to make IT operations better. The central process of AIOps is filtering intelligent signals by removing noise. Therefore, AIOps can solve problems related to tickets, traces, logs, system configuration statuses, incident data, and other related systems.

Why is there a need to remove noise? Collecting too much data can lead to the distortion of our assumptions, understanding, and priorities. It can be difficult to identify the causes of and solutions to certain problems.

Therefore, accuracy is reduced. But with AIOps, ML can solve this problem. This is done by intelligently observing IT operations data, which is then processed quickly to identify the root cause of the problem and find the right solutions. AIOps can solve problems without human intervention.

Defining MLOps

MLOps, short for “Machine Learning Operations,” can be defined as a framework created to focus on the collaboration between operations units and data scientists.

If you know what DevOps is, you can understand MLOps better. In DevOps, there is a need for communication and collaboration between operations professionals and developers. The goals include:

  • Helping manage the production department
  • Decreasing the time needed for a product to go to market
  • Implementing a culture that involves continuous testing, feedback, and development

All these are also present in MLOps, except that the developers in this case are AI specialists, ML engineers, and data scientists.

MLOps is similar to DevOps. However, DevOps focuses on the shortening of the product life cycle while MLOps offers insights so that new procedures can be quickly implemented. MLOps is more experimental compared with DevOps, as data scientists have to try several parameters, models, and features.

AIOps versus MLOps

Clearly, AIOps and MLOps both have some similarities and differences. They should not be confused with one another, as that could lead to inaccurate operations. Both share a willingness to make business systems better and more efficient. AIOps and MLOps may overlap, but they work under different umbrellas and their operations require different approaches.

MLOps aims to close the gap between the operations team and data scientists using ML models in the product development process. On the other hand, AIOps focuses on problem solving through the automation of incident management and analysis of the problem’s root cause.

Final Thoughts

Because they use similar technologies, AIOps and MLOps are often confused with each other. However, we have defined some of their differences in this post.

Both AIOps and MLOps can be used by businesses to improve their processes. The key is to work with experts and specialists who are capable of properly implementing the strategies you are going to use in your operations.

AIOps and MLOps Are Not Really that Different from Each Other.
Loading ... Loading ...