If you work anywhere near data, you’ve probably noticed that the term “MLOps” is getting thrown around a lot lately. By no means is this an accident. By 2024, the global machine learning market is expected to reach $20.83 billion, growing at a CAGR of 44.06%. The top three features enterprises are looking for in data analytics platforms are support for easy iteration of models, access to advanced analytics and a simple process for continuous modifications.

That’s where MLOps comes into the picture. While still in its infancy, MLOps, short for machine learning operations, is just as crucial to making banks’ data science efforts a success as data models themselves. Here’s why.

Something old, something new: MLOps in a nutshell

First, let’s see what all the hype is about. MLOps is essentially DevOps for machine learning models, offering a collaborative approach and tried-and-tested practices for model deployment, including integration, testing and releasing, data governance and performance monitoring. The idea is that data scientists and operations teams join forces to streamline and improve the performance of machine learning models in production.

It also brings together the best of two worlds: innovation and business. “Contrary to what you may think, MLOps allows your data scientists freedom to do what they do best – find answers,” Open Data Science writes on its blog. “Think about it. You didn’t hire your data team to understand the ins and outs of your industry. You didn’t hire them to keep up with regulation. You hired them for their skills in information gleaning. Remove the barriers and let them find your answers.”

Environmental issues: how to make MLOps more efficient

When it comes to implementing and running a banking personalisation platform, finding answers starts during the configuration phase – and never really ends. It’s one thing to set up an algorithm and run a one-off analysis to find out which customers are likely to churn, take out a mortgage or go into the red in the next 40 days. But putting such algorithms into production is another story – and another set of challenges.

The most important being that data scientists must be able to work in a data environment instead of traditional environments.

This usually prompts client-side IT experts to ask: “What’s wrong with dev or test environments?” In short, data, plus security approvals. Dev and test environments contain little to no real-life customer information. Ideally, however, analytics models should be trained on the same data they will be fed in production. Even if it’s not real-time but day-old data uploaded in batches. That’s a win-win for everyone: data scientists can build models more efficiently and won’t mess up the production side if anything goes awry.

Right on track: driving the ML lifecycle

Model lifecycle management is where MLOps really shines. Once a new model has been created in the data environment, data scientists must define which metrics to use for measuring model performance. Candidate models are then approved by legal, data and compliance teams and tested for speed. In the test environment, MLOps will run their own tests and might send models back for fine-tuning. Once they’re polished to perfection, it’s the MLOps team who puts them into production, continues to track model performance and ensures that new iterations don’t disrupt business applications.

Now if that all sounds making an already delicate process even more complex, it’s anything but. MLOps reduces friction between collaborating teams, makes it easier to deploy, optimise and reproduce data models and speeds up time to production and time to value.

Author: Csaba Ragány, Data Scientist

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