This article is primarily written for business decision-makers looking to understand why they should invest in MLOps. For a more technical reader, this article might help identify some arguments if you are trying to convince your managers.

Investing in machine learning will enable you to solve business cases that were previously impossible to solve, for example, automatically categorizing images. Contrary to ML, MLOps in itself doesn’t come with a promise to directly solve any business problems. Rather it comes with the promise to accelerate how your investments in ML return value.

To make an analogy to a more traditional industry, machine learning is shipping goods while MLOps is containerization. And much like containerization of global shipping, MLOps is equal parts process and infrastructure.

Briefly, What is MLOps?

MLOps is, in essence, DevOps but specifically for data science and machine learning.

So, similar to DevOps, MLOps aims to improve the delivery of machine learning models by combining the processes of design, development, testing, and delivery into a singular process. The purpose of the MLOps practice is to increase how quickly and confidently your team can release new ML capabilities.

Benefits of MLOps

While MLOps borrows several pages from the DevOps handbook, there is one massive difference between operationalizing machine learning and traditional software – the data . Software behavior is all about the code, while machine learning model behavior is a combination of data and code.

This combination makes developing machine learning for production much trickier as you have to test and version both, rather than just the code. MLOps as a practice and MLOps platforms can help mitigate many of the symptoms caused by this complexity.

Benefit #1: MLOps Avoids Bottlenecks

One of the most significant issues with machine learning development is that bottlenecks are prevalent, particularly when you’ve got data science teams, machine learning development teams, and operations teams relying on one another for successful software deployment.

When your machine learning software gets held up at any stage of the process, it leads to lost productivity amongst all of your development and operations teams. Without MLOps processes, communication between these teams can be difficult, leading to software being held up for weeks or months.

Machine learning platforms that help you with MLOps can help you avoid bottlenecks by keeping all work versioned, documented, and shared. This makes it easy for your development team to see and understand project progression.

Benefit #2: MLOps Prevents Fatigue

As a manager, one of your job responsibilities is to keep your employees happy and productive. However, when your machine learning projects don’t reach the finish line, get stuck in unnecessary bottlenecks, or end up in “development hell” for months or years, it becomes increasingly frustrating for your development team.

In addition to removing bottlenecks, MLOps practice helps prevent work fatigue in two ways: it guides your team to split work effectively and to automate away manual work. MLOps shifts the thinking from model-centric to pipeline-centric. In other words, the pipeline is the product, not the model.

With the pipeline approach, a team can easily split work and develop parts of the pipeline separately, allowing for more focused, less stressful work. Additionally, when an ML project is thought of as a pipeline from the get-go, it’s only natural to assume that the pipeline will run any number of times. This is a stark contrast to an ML project where the model is the final product, and each new version will be treated as a new project.

Benefit #3: MLOps Implements Stringent Version Control

While all software development projects require that you version control your work, it’s even more vital in machine learning. Machine learning and AI come under tight regulatory and ethical scrutiny because humans do not explicitly define machine learning logic. Thus the predictions served by a model need to be explainable and one of the critical components is telling how the model was trained. This includes versioning both the code and the data used.

Besides regulatory compliance, proper version control for data, code, and trained models helps if there are changes in your development team. Considering that the average tenure of Silicon Valley employees is no more than a few years, there is a significant risk that work is lost when teammates switch jobs. A managed approach to version control helps mitigate that risk, and with the proper tooling, it is mostly automatic.

Conclusion

Pulling the trigger on process and infrastructure investments (enablers) can be tricky as the business impacts are often harder to pinpoint. However, the effects of ignoring such investments will be felt in the long run, with not being able to keep up with the speed of innovation compared to competitors who’ve put every enabler possible in place.

If you want to learn more, we’ve written about how to get started with MLOps .