How is MLOps Used in Business and Marketing?

Chances are, your brand has data scientists and operations professionals on the team, and while they do their best to collaborate, they each have their own areas of expertise.

This could lead to miscommunications and misunderstandings. The data scientists can interpret the data, but they likely don’t have the background to manage business operations. Likewise, the business team can make their side of things work, but they don’t fully know how to interpret and implement data.

Here is where machine learning operations (MLOps) come in.

In this article, we’ll discuss how MLOps can help with collaboration between your data and business teams, as well as additional immediate and future benefits of implementing it.

What is MLOps?

MLOps, in simple terms, is a set of best practices for improving communication and collaboration between your employees on the data science and operations sides of your brand.

In less simple terms, it’s a combination of machine learning, data engineering, and development operations. The goal is to provide a more streamlined process for developing and creating machine learning systems, allowing business administrators, data scientists, marketers, and IT engineers to cooperate on the same level.

It is an extension of what we know as DevOps. DevOps is the process of organizing cooperation between everyone involved in the design and building of big data. This process has been around for a while, but MLOps is still in its youth.

MLOps creates a lifecycle and a set of practices that apply to the development of machine learning systems. This includes research, development, operations, and implementation.

The process of brainstorming, developing, and implementing machine learning is extensive. Having a set of duplicatable processes to guide each project helps in many ways.

5 Benefits of MLOps

Now that we have a general understanding of MLOps, let’s look at how it can impact our businesses. All new technology has either a positive or negative effect on the digital marketing industry, so it’s crucial we understand what we’re getting.

1. MLOps Can Increase Efficiency and Automation

Making the most of our time is something most of us strive to do.

Ironically, we spend a lot of time figuring out how to do this.

We may look into all different kinds of apps and books. We could spend days reading articles or even attending seminars on efficiency and time management.

But, perhaps automating would be simpler for many people.

Automation, by definition, should increase efficiency. Once minute tasks are out of people’s hands, they can focus on big picture issues.

An essential but draining task many folks on data science teams spend time doing is data entry. That’s time they could spend focusing on the science rather than passively improving their typing skills (and, we all know, a typo in data entry can bring disaster!).

MLOps gives options for automating tasks like data entry. It takes some work upfront, but once things are going, the data scientists can get back to what they do best.