With all the new AI and machine learning-related terminology floating around these days, it is easy for non-experts to get lost in the jargon. With the number of acronyms used in the field, keeping track of all of them can sometimes seem an impossible task.

Fortunately, the major components of machine learning (ML) and its operational branch (MLOps) are not overly complicated if they are broken down logically. And a related field—that of development operations (DevOps)—is also becoming big news on the tech scene. Many people are confused about the difference between DevOps and MLOps because they contain similar characteristics and perform somewhat related functions. However, these are two distinctly different fields with their own specificities. Let’s take a look at both of them and see how each set of operations breaks down.

What is machine learning, and what is MLOps?

Machine learning is a component of artificial intelligence that uses algorithms to try to imitate human behavior in machines. It is being used in more and more industries, including medicine, finance, logistics, and manufacturing.

Machine learning operations is, as the name suggests, a way of operationalizing machine learning. MLOps use what are known as CI/CD (continuous integration and deployment) mechanisms in their functioning. CI/CD practices are used in software to enable code changes to be made faster and more effectively than they otherwise would. In automating these processes, CI/CD allows changes to be made without running the risk of human error. This practice is making a big impact on ML functioning overall, both because of the amount of time it saves practitioners and the efficiency of being able to automate code changes.

What is DevOps?

Development Operations, or DevOps, is also a set of practices that software engineers and IT specialists work with. Their aim is to streamline and further develop the relationship between these two specialties and the people who work in them.

What this means in practice is that there are a set of functions that work on application development, automation, and programmable infrastructure. DevOps aims to refine all aspects of software development, from the software itself to the relationships between people working on any given project. There are four major components of DevOps:

  • Working with CI/CD frameworks. This generally refers to the servers on which new software is developed and tested.
  • Source control management tools. These are tools that track code changes which take place in the course of a given project cycle.
  • Automation tools. Automation tools serve the purpose of taking the application code used in the development of a given piece of software and putting it together into usable form.

In short, DevOps has to do with the whole system of software development, including the product itself, and the individuals involved in creating it. MLOps, on the other hand, focuses on machine learning, in particular, and attempts to create human-like capabilities in machines.

The difference between MLOps and DevOps

Beyond their basic characteristics, there are a number of ways in which MLOps and DevOps can be distinguished in terms of their functionality. A look at several aspects of the operations processes will reveal more specifically how these two practices differ in the way they work.

Continuous monitoring

One of the features of both practices that illustrates the differences between them is what is known as “continuous monitoring.” Continuous monitoring in DevOps refers to the fact that DevOps projects, once finalized, are considered complete and in the future will be looked upon as accurate in their original forms. MLOps, on the other hand, does not necessarily produce immutable products because the factors surrounding their development are subject to change over time. MLOps models create new predictions when new data is available to fill them, and therefore they need to be monitored as new versions come out. They are seen as being subject to degrading over time. Once a software version is created, on the other hand, it is considered complete. Therefore, further monitoring is not necessary.

Versioning

Another difference between DevOps and MLOps can be seen in versioning. Versioning in DevOps is fairly straightforward, as the changes in code are kept track of, but DevOps doesn’t require maintaining discrete models of versions. In MLOps, on the other hand, versioning needs to be monitored very carefully. Because there are a large number of variables going into the creation of any given model, versions must be kept frequently and with detailed information included so that conditions are kept constant. This includes the tracking of datasets, hyperparameters, and performance indicators.

Workflows and project templates

Another way in which the two operations types differ is in their approach to workflows. In DevOps, workflows are fluid because other factors that go into their operations are constant. In MLOps, however, workflows should be kept uniform because of the number of changing variables among datasets and other factors. MLOps also utilize project templates, with each specific to the needs of a given project.

Development will continue

Hopefully these descriptions help clarify the difference between these two practices. Again, machine learning operations are more focused on the process of building models that can be changed and enhanced over time. Development operations, on the other hand, exist for the purpose of creating distinct pieces of software for particular purposes. Both are rapidly growing fields with wide applications across industries.

These terms will probably continue to change in both definition and application as MLOps methods grow and new means are developed to create and preserve models. Nonetheless, it is good to have a solid grounding in both concepts, as they will surely be used with increasing frequency across many different industries soon.