Operationalization(O16N) and Machine Learning Operations (MLOps): What Is The Difference Between These Two Buzzwords?


Operationalization(O16N) and Machine Learning Operations (MLOps): What Is The Difference Between These Two Buzzwords?

xpresso.ai Team
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The machine learning space is filled with new buzzwords every single day. It might be difficult to get a handle on what these terms actually mean, but fret not; help is on the way. We will show you the differences between MLOps and Operationalization. The difference is subtle, but these terms are sufficiently different to make a difference inside your enterprise AI endeavors. The first thing to note is that these two terms are quite similar in what they define. In fact, you might find that the differences between them are in the level of the organization most likely to use each method. MLOps typically focuses on the technical side and is usually the concern of engineers and scientists. O16N focuses on the managerial and business side.

What is MLOps? Machine learning operations is a broad term that essentially means DevOps for machine learning projects. It is an entire methodology of putting machine learning models into production. This methodology focuses on the tools, and you are likely to see it in the technical people on your team. 

Operationalization mostly has to do with what management and other parts of an organization have to do to put those machine learning models into production. As mentioned above, you can think of operationalization as MLOps from a management perspective.

What Is This New Field of MLOps

You can see from this chart that MLOps is a way of making enterprise AI more like the software engineering field. There are various MLOps tools that are used to make this process in reality. It contains flexible tools that enable organizations to go through the entire pipeline quite seamlessly. The best part of MLOps is that it is mostly focused on the engineering and technical side.

The mathematical and technical sides are the most crucial parts of the process, as they determine how seamlessly an algorithm will be implemented. It is vital that all aspects of a project be managed holistically, but you cannot get things done if your technical people are not knowledgeable enough. There’s also the major factor that data scientists and machine learning engineers have to come together to productionize ML models. What this means is that they have to create the workflow that gets the project from point A to B. There are various MLOps tools that are used to make this a reality. MLOps best practices are also being developed over time, which will come as the industry matures even more.

As you can see below, machine learning operations can be tricky when all things are put into place. It is that technical difference that is apparent in Operationalization and MLOps.

Operationalization: How It Differs From MLOps

Operationalization has many overlaps with MLOps, but there are a few differences that make the difference. The main difference has to do with the focus on the business side of your machine learning operations. For example, what will the business impact be? Answers to these questions are usually answered by the managers and executives in the company. They are usually the ones focusing on a 3,000-foot overview of what is going on. They look at the business impact that your enterprise AI efforts will have. They also look at non-technical aspects like legal and data governance. The managers are also there to ensure that the various people in the organization are working together to achieve the right goal. For example, while MLOps might focus on the performance of the data version control system, O16N will be focused on the hiring resources put into place to facilitate acquiring talented individuals. The CEO is more likely to be involved in the O16N side of things. Things like hiring, the structure of your teams, and how you measure ROI make up O16N.


Now you have gotten a general overview of Operationalization and MLOps. As you can see, there are large overlaps in the various methodologies we are talking about. These overlaps have to do with the layer of a corporation focused on the machine learning operations that are currently happening. To be successful, it is crucial that your organization focus on both aspects. MLOps covers the technical side, and operationalization covers the business and managerial side. When they work together, your organization will find massive success.

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xpresso.ai Team
Enterprise AI/ML Application Lifecycle Management Platform

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