MLOps vs DevOps: Similarities & Differences in 2024

You can train a couple of machine learning models that you use for your business, bring them to life, and monitor the results manually. But what if these models multiply? Managing and monitoring multiple models developed by multiple teams requires a systematic approach. MLOps emerges as a response to the search for the best practices for streamlining the machine learning model development and management process within an organization.

MLOps accelerates the process from design to production of models by bringing together many business divisions such as IT professionals, data engineers, and designers. MLOps is also referred to as DevOps for machine learning. In this article, we will explore the similarities and differences between MLOps and DevOps. 

What is MLOps?

MLOps is a set of tools and best practices for bringing machine learning into production. As can be seen in Figure 1, coding is only a very small part of the entire lifecycle of a machine learning model. MLOps practices are intended to streamline coding as well as other processes within the ML lifecycle.

Figure 1. Components of an ML system

The machine learning lifecycle can be summarized as follows:

• Understanding the problem: Detailed exploration of the problem to which the solution will be applied.
• Data collection: Data that can help in the solution are collected and various operations are performed to bring them into a usable format in the model building section.
• Model creation: Developing the ML model and training it with prepared data.
• Model deployment: The model is deployed in production.
• Model monitoring: The model is monitored against performance degradation such as model drift.

MLOps covers the entire lifecycle and enables faster model development and deployment by: 

• Automating as many processes as possible within the ML lifecycle,
• Creating a unified framework to follow within an organization to improve communication and collaboration about ML development.

What is DevOps?

DevOps refers to a working discipline that enables the development and operation units in the IT department to work closely together and achieve more efficient results. DevOps practices make software development faster and more efficient by bringing two different units under one roof and facilitating collaboration between them. Therefore, the progress of products in an automation environment ensures that all personnel in DevOps are aware of the application’s coding, running and lifecycle as deployment, release, and test processes.

Figure 2. How DevOps works

DevOps involves approaches such as continuous integration (CI) and continuous deployment (CD). These approaches enable faster and more streamlined production and testing processes by automating the testing and validating code changes from multiple developers and integrating them into a single software project.

MLOps versus DevOps

Both DevOps and MLOps strengthen and promote teamwork between people who develop, people who operate, and other stakeholders. Both emphasize process automation in continuous development to gain speed and efficiency. But as mentioned above there are slight differences between DevOps and MLOps as machine learning discipline is required.

MLOps brings the efficiency gained by DevOps practices to the ML lifecycle. However, it does this by adding the continuous training rule in addition to the CD/CI approach. In DevOps, since the version offered by the software team does not change after testing and deploying, a working output is obtained for all users. However, this is not the case for machine learning models. 

Data is constantly changing and the ML model adapts to it. Therefore, with continuous training, ML models in production retrain automatically after data changes. MLOps’s ability to continuously train the model is also available in areas such as monitoring and testing. MLOps are constantly trained for the model to work more efficiently and properly.

If you want to get started with MLOps in your business, you can check our data-driven list of MLOps platforms. If you have other questions, we can help: