MLOps: Why companies don’t want end-to-end solutions

Many companies are integrating machine learning into their applications. For example, major brands like Instagram use machine learning in their recommendation systems for users. This added complexity has meant the need to create something similar to what software engineers have in terms of the data science pipeline. In software engineering, DevOps engineers use automation tools to build, test, and deploy applications. Companies implementing machine learning into their applications realize that they need end-to-end solutions like what DevOps engineers have for their organizations.

What Is MLOps?

MLOps is a way for machine learning engineers and data scientists to solve the same problems solved by DevOps engineers on a team. They are building the ML pipeline needed to automate a significant portion of the machine learning and data engineering process. We can say that MLOps is simply DevOps for machine learning. MLOps also makes it easier for data scientists and engineers to work more closely together. Previously, data scientists did all the preprocessing necessary to build models and come up with predictions. Those predictions would then be implemented into the application by data or machine learning engineers. However, there is a massive disconnect between them. There are no tools to automate the process of turning those predictions from data scientists into deployment-ready models for machine learning engineers to use.

Why Do We Need MLOps?

Machine learning operations streamlines the data science pipeline. It provides the same benefits for machine learning that DevOps has for traditional software engineering. Since machine learning and data science are relatively new fields, it is a lot more challenging to find competent engineers who understand the entire lifecycle from data collection to system deployment. These tools allow the data scientists and ML engineers to work together on a single playing field where they can collaborate. Traditionally, data scientists worked in software programs such as R, while machine learning engineers would work in production systems like Java with Hadoop.

The Data Science Pipeline in Machine Learning

The data science pipeline is an integral part of machine learning applications, and it is currently not in the domain of machine learning engineers. This pipeline involves data exploration, preprocessing, modeling, and prediction building. It is only after the steps in this sequence have been taken that the data and results can be passed on to the machine learning engineers to implement the findings in the application. MLOps involves all the steps necessary to make this sequence simple to implement, and it also involves automating certain aspects of machine learning engineering as well. For example, the model training, evaluation, and deployment that machine learning engineers need to do.

Why Don’t Companies Want End to End Solutions?

The major reasons why companies are not looking for end-to-end machine learning operations solutions have to do with the current landscape. There are not enough mature systems, and the available ones will not suit large companies. These companies prefer to get off-the-shelf software and use that to plug the gaps they have in their pipeline. The industry’s current immaturity means that it will take a long time for complete and robust enter and solutions to come about that will fulfill the needs of major corporations who have the resources to create their own custom solutions.