The Key Issues That Make MLOps Different from DevOps
MLOps has risen in popularity in recent times. It is often compared to DevOps; however, there are a few key differences you need to be aware of. These key differences make MLOps wholly incompatible with the workflow in DevOps. The main issue has to do with the nature of machine learning projects. The MLOps workflow is a lot more complex, and it requires more pieces to come together to get projects off the ground. It is one of the major reasons why machine learning projects only make it into production 87% of the time.
The DevOps workflow focuses on tools to automate the building and testing of software projects. That is because software projects are usually only code. Because of that, you only need to focus on tools to test and deploy your code. It makes the DevOps pipeline more streamlined. It also means you don’t have to focus on many of the complexities that make machine learning so difficult.
Machine Learning Projects are More Complex
Complexity is a major problem with machine learning projects. The main problem has to do with finding and cleaning data as well as turning that cleaned data into a machine learning model that is ready to be deployed.
You also have the fact that MLOps projects require two different professions to work. It starts with the data scientists building the models that machine learning engineers then implement. Throughout this process, there is a lot of testing and repetition that must take place. Many steps are repeated until it gets to a satisfactory result. This complexity is more difficult to manage, which is why MLOps is so much more complicated than traditional DevOps.
MLOps Isn’t as Mature as DevOps
The other factor is maturity. Mainstream machine learning is relatively new. The tools to successfully create a machine learning project from start to finish are relatively new. There is also the fact that the version control system in MLOps is not just concerned with code. You also need to keep track of different versions of your data and models. This added complexity is enough to completely change the nature of MLOps.
On top of all the reasons listed above, you also have to think about where the data you use will come from in production. It means taking things like model drift into consideration. These different factors make a big difference when working with most machine learning projects. It means that the concepts in DevOps are not enough to keep up with the requirements of MLOps.
Machine Learning Requires More to Bring to Production
As mentioned above, you will need much more than code to bring a machine learning project to production. The increased requirements will forever make MLOps a different beast to DevOps.
However, as MLOps tools improve, they will become a lot more streamlined. We might even come to a time when deploying machine learning projects is as easy as software engineering projects. Until then, there are tools like xpresso.ai to automate a significant portion of the process. It cuts down your startup time dramatically.