Global spending on AI is higher than ever before—IDC reports that it’s expected to top $85 billion in 2022. However, only 13% of machine learning models actually make it into production, and less than 1% have their intended impact.
So, where’s the disconnect? And how can organizations more effectively operationalize models so that they can add value to their business?
Enter: ModelOps. You’ve probably heard the buzzword, but here we’ll break down what ModelOps is and how you can leverage it (if you aren’t already!) to benefit your organization.
What Is ModelOps?
ModelOps, short for model operationalization, is a system for automating the development, deployment, testing, monitoring, and continuous improvement of AI and ML models across teams and platforms. Perhaps the best aspect of ModelOps is that it makes maintaining and improving models easy at scale.
When organizations leverage ModelOps, it helps teams spend less time on mundane tasks and focus more on continuously improving business operations (with the help of their freshly deployed models, of course).
How Is ModelOps Different from MLOps?
If you’ve heard of ModelOps, you’ve likely also heard the term MLOps. While they’re closely related, the major distinguishing feature between the two is that while ModelOps operationalizes all AI models, MLOps only refers to the operationalization of ML models.
ModelOps is simply a widening of MLOps—it does everything MLOps does, with the main priority being to constantly re-train models so they’re achieving peak performance.
What Is the Significance of ModelOps?
Dresner’s 2022 ModelOps Market Study states as enterprises use more and more analytical models, the requirement for managing models more deliberately with ModelOps has only continued to grow.
ModelOps is essential for organizations to scale their analytics capabilities by transforming lines of code on a data scientist’s laptop into models in production. Here are ways effective ModelOps can empower your organization.
Close the Gap Between Model Deployment & Governance
MIT Sloan Management Review reported that 26% of companies have AI in widespread production in 2022 (up from just 12% in 2021). Still, most enterprises are far from leveraging AI for significant outcomes, and deployment issues were among the biggest challenges hindering this goal.
On top of that, governing models is more important than ever before—having complete visibility into who has access and who’s in charge of tracking a model’s results is essential to mitigating potential risks to the business.
ModelOps aims to address both pain points, ensuring all models reach production and that they’re managed according to pre-established processes.
Detect Model Deterioration
One of the main reasons you can’t just sit back and relax after your models have been deployed is model drift. When the pattern a model is designed to respond to shifts, drift (also called drift of concept) occurs, deteriorating a model’s predictive power.
To avoid drift, models need to be maintained and retrained with new data regularly. ModelOps can help detect and mitigate drift and biases in machine learning models before they start to affect the model’s success rate. It also introduces intuitive monitoring, which allows organizations to monitor their ML models and get alerted to real-time deterioration so they can proactively retrain the model.
Improve Model Output Quality
As we just mentioned, small changes in data can have a significant effect on models if they’re not properly detected. And the only thing worse than not getting a model into production is having a model in production that’s inaccurate and contributes no value to the business.
Because ModelOps relies on having quality data, it’s more likely to result in high-quality model outputs. Having quality standards for what’s considered a “good” model output for each individual model also helps make sure everyone in your team is on the same page.
Provide Continuous Monitoring
As an automation capability, ModelOps works in real-time, constantly providing comprehensive metrics that enable data scientists to continuously monitor model performance, accuracy, and data quality.
Models can be evaluated using common metrics such as FI score, Area Under the Curve (AUC), the Receiver Operator Characteristics (ROC), and more. All these factors are evaluated regularly to ensure that necessary changes are implemented as soon as possible—if an anomaly in a model’s output is detected, it can immediately be investigated. ModelOps also enables improvement via retraining and redeployment.
Use Cases for ModelOps
ModelOps isn’t just specific to one industry—it can be utilized extensively in a variety of sectors, including:
Finance
As one of the most highly regulated industries, models created for financial services institutions need to be fair, robust, and built to detect and avoid bias. These models should also be retrained regularly to stay compliant, secure, and up to date.
Revolut, an e-money institution (EMI), wanted to help their customers get the most out of their money, all through their app. Fraud prevention is, of course, top of mind for them, so they implemented MLOps on their fraud detection machine learn model. Any time their model suspects a fraudulent transaction, the user is asked to confirm. Their response not only resolves the issue for them, it also helps to continuously improve and train the model as well.
Retail
Retail was one of the industries that the COVID-19 pandemic hit the hardest. The uncertain, tumultuous consumer behavioral patterns made it difficult for companies to develop, deploy, and monitor AI solutions effectively. ModelOps can help retailers automate and standardize ML models so they can monitor them and access key metrics less manually.
Ocado Retail is the perfect example of a ModelOps success story—they launched a feature called “Smart Shop” where consumers can create an online shopping basket in one click. At the start, they were struggling to create a personalized “best basket” of groceries for each customer. With ModelOps, they were able to quickly launch a model that analyzed customer shopping behavior and to continuously improve that model until they had a feature they (and their customers!) were happy with.
To Conclude
Lots of enterprises struggle to get their models where they want them to be—at peak performance, generating invaluable insights for their organization. Automating model creation, deployment, and monitoring with ModelOps can help you get there.
If you haven’t implemented ModelOps, what are you waiting for? Check out RapidMiner’s page on MLOps to see how our platform prevents model drift, minimizes development friction, and customizes insight delivery.
