Operations Research Should Join the MLOps Party

Widening the MLOps Umbrella

Data science (DS) and operations research (OR) are both founded on the application of mathematical logic and optimization to business problems. While the core models and applications for these two fields do differ, their underlying workflows for obtaining answers share a very uncanny resemblance. Both disciplines collect and preprocess data, and they create mathematical models. The models are evaluated for performance, and passing models may be deployed to generate outputs for downstream users. For both DS and OR models, the process is both cyclical and iterative, as changes in the business landscape require continual reassessment, and as research insights lead to better approaches.

The DS community is currently in the middle of an operations revolution, which has rapidly enhanced practitioners’ ability to deliver business value. The high level similarity in DS and OR workflows means that lessons learned in DS operationalization can also be applied to OR applications, resulting in large additional business value gains.

A Quick Operations Research Review

Today, the OR acronym isn’t as widely known as artificial intelligence (AI) or machine learning (ML). Many people, including many data scientists, don’t understand how widely OR is applied, or how core OR is to modern business use cases.

OR debuted well before the digital revolution, proving its value as far back as World War II. In peacetime, companies found that they could use OR to greatly improve business processes through the use of mathematical optimization and logic.

The OR field is large and well established. Most large companies have personnel dedicated to OR, sometimes under the names of management science, or decision science. OR is known for reliably solving problems in financial engineering, industrial engineering, management operations, and supply chain management. If you are envisioning “core decisions” and “big business outcomes,” then you are on the right track.

Many well-known universities have OR programs, and they are increasingly embracing DS and ML as part of their curriculums. A few examples include:

• MIT Management Sloan School: Operations Research and Statistics
• Princeton: Operations Research and Financial Engineering
• Cornell Engineering: Operations Research and Information Engineering
• Carnegie Mellon, Tepper School of Business: Ph.D. Program in Operations Research

At the time of writing, each of these websites had a reference to DS or ML on their home pages, underscoring the natural relationship between the disciplines.

In 2021, US News and World Report listed Operations Research Analyst at #5 for business jobs, and #13 for best STEM jobs, with a median salary of $84,810, a 1.1% unemployment rate, and 26,100 jobs. In comparison, Data Scientist is listed at #2 for best tech jobs, and #6 for best STEM jobs, with a median salary of $94,280, a 3.5% unemployment rate, and 10,300 jobs. The reports suggest that, at least by labor, the OR industry is more than double the size of its DS counterpart, with less pay, but also less unemployment.

MLOps is Supercharging Data Science

In contrast to OR, the DS field got its main start when large tech companies experimented with better ways to generate value from patterns in their large amounts of data. Before DS, a lot of data research happened slowly and more independent from the business environment. For example, data samples from a company problem might be shared with an academic institution in the hopes of gaining insights many months in the future.

The early DS innovators were highly motivated to solve practical problems within the business environment. The new approach took advantage of data that was bigger, better, and more timely, and because it was in-house, it benefited from higher quality communication. These early tech pioneers also had close proximity to key advances in data processing and programming technologies, which also happened to coincide with key advances in optimization and pattern recognition theory.

The DS field is still actively developing. Leaders in the community have moved from small modeling experiments on laptops, to huge modeling workflows in production cloud environments. There are still many businesses struggling with this type of transition, however, the opportunities and financial incentives prove to be strong motivators.

The tools and methods of operationalizing DS models have coalesced under the MLOps name — short for machine learning operations. Many excellent tools now exist for making DS efficient, consistent, and dependable for business. Feature management, training with cloud resources, keeping track of model versions and performance — these can all be automated and orchestrated using MLOps tools in the cloud.

A key part of my work on the Maven Wave DS team is helping clients to take research-oriented DS initiatives, and transform them into more viable production-ready, profit-generating, business solutions. Companies often struggle with their high DS spend to low DS output ratios. My team applies MLOps best practices and technology in ways that reduce processing time, improve model selection, allow less room for error, and make deployment easier. We also help DS teams standardize and streamline their processes, so that they are easier to maintain, manage, and monitor. We’ve seen MLOps supercharge DS solutions, making them more accessible and valuable to their enterprises.

Connecting the Dots Back to Operations Research

The huge advantages of modern MLOps approaches are now discussed regularly within the data science community. Many companies are currently moving to add cloud-based MLOps pipelines to their workflows. The pipelines and tools have catered specifically to DS models and applications. However, due to the similar underlying structure, many of these ideas and tools stand ready to benefit OR workflows as well. Granted, some tools must be adapted, but we can make this happen, and it is a small price to pay for the abilities on the other side.

I recently had the opportunity to lead a modern MLOps-style engagement on an OR problem for a large enterprise health care client. The company, a trend-setter in its industry, still used traditional workflows to run a massive linear programming model for drug price optimization. The existing solution took many hours to run, and it was plagued by manual configuration steps with the potential for human error. The process for obtaining price outcomes was unnecessarily variable and complex, and it required a lot of maintenance and review time from senior OR practitioners before business results could be obtained.

My team used MLOps practices to consolidate and automate many of the steps in the OR workflow. For this client’s case, we found a way to use Kubeflow and Google’s AI Platform — tools originally built for doing ML work — to create linear programming constraints on parallel distributed nodes. The linear programming model itself was constructed using PulP, which is a popular library for OR model building. In the end, we successfully married the MLOps approach to the OR application for a power set of results.

We built a pipeline that ran in less than 10 minutes; it accepted a few essential parameters at runtime, and it finished by saving a model and writing results to BigQuery for easy reporting and analysis across runs. The pipeline scaled for parallel on-demand runs in the cloud, and it released resources upon completion. Our solution meant lower IT costs, fewer manual errors, standardized examples for future work, as well as more timely and accurate results for our client’s customers.

Many enterprise OR teams have worked with traditional on-prem platforms and toolsets for years. Some of these teams are undoubtedly still attached to their legacy systems. However, as the cost to switch to more modern approaches goes down, and as the competition to do so heats up, I predict there will be higher pressure on OR teams to move their solutions towards more agile, cloud-based workflows. Some OR teams have already experimented with such solutions, however, the field stands to benefit from the standardization that an MLOps-style approach provides.

An Invitation to the MLOps Party

The opportunity is here for enterprises who wish to move their OR workflows from the traditional paradigm with tedious work and longer wait times, to a more streamlined and standardized paradigm. MLOps tools originally developed for data science workflows can revolutionize OR workflows as well. Moreover, the lessons learned solving data science problems, mean the OR industry stands to reap high returns for low risk by emulating the MLOps approach.

Maven Wave has experience migrating enterprise on-prem OR applications to cloud-based pipelines. I invite companies employing OR optimization techniques to consider joining the MLOps party, and I believe there are great rewards waiting for you here.

The Maven Wave data science team regularly strategizes to maximize impact. If you are interested in working with us to build your future world-class implementations, please reach out.