MLOps vs DevOps: Let’s Understand the Differences?

In this article, we will be going through two concepts MLOps and DevOps. We will first try to get through their basics and then we will explore the differences between them. As you might be aware in DevOps we try to bring together the programming i.e development of web app or any software, it’s testing mainly done by QA people and then its deployment. MLOps as well share similar objectives. There is a whole machine learning model development life cycle that we try to streamline. So here in MLOps, we are trying to stitch this lifecycle to make a coherent process that works with minimum or very less hiccups. Let’s dive into the details of each of these concepts and then we will try to understand key differences between them.

DevOps is a practice where people work in a team to build and deliver software at the best possible speed. DevOps enable software developers(devs) and operations(Ops) teams to fasten up the delivery of Software through collaboration, and in an iterative manner. DevOps methodology helps improve communication between your developers and ops working on projects. It best serves the following purposes:

From this figure above we can understand the whole DevOps process. Organizing tasks and schedules and other stuff starts with this very step called plan. Planning starts according to the user stories made in every sprint if you are using agile methodology. Then starts development or coding part of the software. Testing is done of the application developed so far for any bugs. Once code passes this stage of testing (or continuous integration) it is sent for deployment. In the next step, Ops maintain infrastructure and truncates any vulnerabilities or security issues from the software. The last stage is to monitor the application developed for fixing the hiccups to ensure a smooth end-user experience.

So, I hope with this you are now clear with what exactly is DevOps. Let’s now understand what is MLOps…

In DevOps, we saw that it was for streamlining software development and then deploying and monitoring them. In MLOps we focus on Machine Learning Operations. So, the guys who are involved in this methodology are data scientists, IT, and DevOps Engineers. It is a useful approach for creating best-in-class machine learning solutions for the end-user. For developing machine learning solutions the standard lifecycle goes like this: So from this whole pipeline, it is understood that developing models is just a very small part of the whole process. Many other configurations, steps, processes, or tools are to be integrated into the system. For this streamlining, we have this machine learning development methodology MLOps. MLOps also provide the same benefits as in the DevOps.