The Business Value of MLOps

Sure, Machine Learning Operations (MLOps) is great for the development, deployment, and maintenance of ML tools. But what does the business side of things get?

Recently, we wrote an article on how MLOps can solve many AI adoption problems. In that article, we talked about the key benefits of MLOps from a data science perspective.

We defined MLOps as “the collaboration of data science and IT teams with the goal of improving the delivery and maintenance of machine learning tools and models. It includes testing and validating models, deploying them, and continuously improving them via the CI/CD (Continuous Integration and Continuous Delivery) model.”

While this sounds like a major win for the teams involved in producing and supporting machine learning tools, it doesn’t directly address the benefits that MLOps can bring to business teams. In today’s article, we’ll review reasons why MLOps can boost business performance.

4 Ways MLOps Adds Business Value

It can be very hard to get an AI or ML project online, let alone getting value from it. The reasons for this include the amount of expertise required (in a variety of fields), problems with AI models’ real-world performance, and a lack of communication between technical, data, and business teams. As you can see from the above description, these are exactly the problems that MLOps is designed to solve.

#1. It Makes ML Solutions More Accessible

So, we can chalk the first business win for MLOps up to simply making the ML lifecycle more effective. As business users are the ultimate consumers of business ML products, this is a big win indeed.

MLOps also borrows the continuous integration and deployment idea from DevOps; this makes MLOps more scalable, which means ML solutions can be adapted to other departments and use cases. While we might count this as a major plus for the ML teams (who don’t have to build a brand-new tool for every use case), it also makes deploying ML across the organization faster and more efficient. It’s a win for the teams who don’t have to wait around for their unique needs to be met by a new build.

#2. It Adds Much-Needed Flexibility and Scalability

In a related vein, MLOps also provide more flexibility, so models can also be adapted to changing market conditions (for example); this helps businesses themselves be more flexible and adaptable.

One of the major impediments to ML and AI adoption has been underperforming models. However, this is another thing that MLOps was designed to solve. Thanks to automation, models can be continually monitored; when problems arise, changes can be made and quickly deployed to end users. In this case, no one is stuck with inaccurate or unreliable models (which, in business terms, translates into unusable tools). Adjustments can be made to bring out the true power of machine learning and put it into the hands of business users.

#3. It Helps Reduce ML Costs

So, with MLOps, the teams responsible for building, testing, deploying, and monitoring machine learning tools can work faster and more efficiently. They’re not scratch-building new solutions for each new use case. They’re employing automation and other techniques to reduce some of the monotonous recurring parts of their workload. For the business side, guess what this adds up to? Savings.

Machine learning and AI are rapidly becoming standard office tools; they’re not the big-enterprise-only luxury that they once were. And this means that there has to be room in the budget for them. But with MLOps, unnecessary costs can be shaved off as teams operate as efficiently as possible.

#4. It Provides a Better User Experience

Mobile apps are famous for updating themselves every few months – or maybe more. And there’s a good reason they do: it’s very hard for designers to cover every possible use case until they can get a product into the hands of the actual users. Of course, app development offers the flexibility that allows developers to push new changes as the need arises. But for many AI and ML tools, this wasn’t possible: the functionality you got at the beginning was not changing any time soon. And this could apply to performance as well as aesthetics and user experience.

Well, this is another way MLOps can bring business value. Because of the CI/CD process, the ML development team can do more than just fine-tune model performance; they can ensure the user experience also meets users’ needs. And this is another way to increase the adoption of AI and ML tools.

MLOps as a Key to Enhancing Business Performance

In this article, we’ve focused on the “big four” non-technical ways that MLOps can add value to businesses. There are, of course, several others. But the main takeaway is that MLOps does more than make building machine learning faster and less cumbersome. It can affect the agility and efficiency of the entire enterprise.

Authored by: Anil Kaul, CEO at Absolutdata, an Infogain Company and Chief AI Officer at Infogain and Rajat Narang, Associate Vice President, NAVIK Strategy and Product Management at Absolutdata

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