ModelOps refers to a framework for developing, deploying, and managing artificial intelligence (AI) and analytics models of all kinds. Its primary goal is to help enterprises build AI models that quickly progress from ideation to production as efficiently and responsibly as possible through greater governance and monitoring.
It’s easiest to think of ModelOps as a variation of the widely recognized development operations (DevOps) process. While DevOps is mainly concerned with application development, ModelOps is designed specifically for data analytics and artificial intelligence.
According to IDC research, worldwide spending on AI solutions will surpass $500 billion by 2027. However, by 2025, at least 40% of Global 2000 organizations will have already allocated at least 40% of their core IT spend to AI initiatives—a move that will generate double-digit increases in the rate of product and process innovations.
Given this sizable shift in the weight of enterprise technology investments, ModelOps solutions are quickly becoming essential to AI/ML deployments.
ModelOps vs. MLOps
MLOps encompasses the entire process of developing, deploying, managing, and optimizing ML models. That includes everything from data preparation and model training to deployment, monitoring, and continuous improvement. Notably, as the name implies, it focuses specifically on machine learning models.
On the other hand, ModelOps normally emphasizes the latter half of life cycle management—namely, model monitoring and maintenance. Moreover, it doesn’t limit itself exclusively to ML models but includes all forms of artificial intelligence and data analytics.
Target audience is another key difference. MLOps leans heavily in the direction of model training and development, which means its primary users are data scientists and engineers. ModelOps, by contrast, is intended to be used as an enterprise governance capability. Therefore, it’s normally owned by the chief technology officer (CTO) or a similar executive.
Bottom line: ModelOps is best understood as a subset of MLOps that strengthens the operational aspects of models in production. MLOps is a more comprehensive field that covers end-to-end lifecycle management.
Why ModelOps matters
According to a 2022 Gartner survey, as reported by VentureBeat, just 54% of AI models make it into production. Another survey indicates the problem may even be worse, as just 26% of respondents said their organizations’ AI initiatives reached deployment.
Indeed, despite their growing interest, many enterprises struggle to get their AI and machine learning investments off the ground. Why? Several challenges complicate the process, such as:
- Uncontrolled data growth. Eighty percent of data scientists spend their time preparing data during analytics projects rather than focusing on value-added work. Although vital to the end product, manual and time-consuming data preparation greatly slows down the production cycle.
- Complexity. Model deployment is exceptionally difficult at the enterprise level. Given the size, scale, and impact on numerous business systems, it takes a typical organization months to operationalize AI/ML models—and even then, the work isn’t over.
- Monitoring. AI initiatives require constant maintenance and retraining to ensure adequate model performance. Without proper monitoring, issues can go undetected and cause further problems down the line.
- Compliance. As artificial intelligence evolves, so do increasingly strict governance standards. Adhering to regulatory requirements and ensuring responsible AI usage requires a level of visibility many data science teams don’t have.
Fortunately, these issues are exactly what ModelOps solutions are designed to mitigate. Regardless of model type, whether for deep learning or predictive analytics, organizations can streamline and simplify their efforts through ModelOps best practices.
First, the framework provides thorough governance. The entire point of ModelOps is not merely to deploy analytical models, but also to achieve visibility into the entire enterprise.
To that point, ModelOps practices uniquely lend themselves to enterprise use cases. They’re meant to be used by executives, data scientists, and information technology (IT) personnel alike, enabling collaboration and cross-departmental synergy. ModelOps offers executives the visibility to see AI initiatives through to production while generating tangible business results.
The key components of ModelOps
Generally speaking, there are three elements vital to the ModelOps framework. These include:
- Model development
Once the dataset is ready, teams use algorithms to train their model to solve the identified problems (or perform other predetermined tasks). Following that, processes include:
- Testing and validation. Data scientists evaluate model performance by predicting its output in situations outside the training data. During this process, teams experiment with different algorithms and architectures to identify the best-performing model. They use a separate dataset—a validation set—to tune hyperparameters and change the model’s architecture.
- Versioning. Teams must track and manage changes to their models and corresponding dependencies. This enables them to roll back to previous versions, compare performance over time, and ensure reproducibility.
- Model deployment
Finally, it’s time to deploy the model into production. At this stage, organizations integrate AI solutions into their business environments and critical systems.
Containerization is one way to deploy a model. During this process, data scientists package the model and its dependencies into a container, making it easier to roll out at scale. Moreover, by using various data pipelines, teams accelerate model deployment and reduce the time and effort required to activate them.
- Model monitoring
Monitoring is perhaps the most important aspect of ModelOps, as it directly impacts long-term model performance. A robust ModelOps tool can automate this step, effectively updating models post-production and reducing the amount of maintenance required.
ModelOps benefits and use cases
Any organization invested in AI initiatives or actively exploring their capabilities should strongly consider leveraging a ModelOps platform. The right tool can help enterprises unlock numerous advantages, such as:
- Improved scalability. ModelOps streamlines end-to-end life cycle management, making it easier to launch analytical models at scale. This is especially crucial for handling large datasets and effectively integrating models into production environments.
- Greater agility. An advanced ModelOps platform can ensure AI/ML models quickly adapt to changing requirements, which is especially important to predictive analytics, where systems need frequent updates to uphold performance.
- Enhanced visibility. Not only do ModelOps practices support continuous monitoring, but they also help business leaders ensure AI/ML investments lead to tangible business outcomes.
- Reduced cost. ModelOps can significantly reduce time, effort, and expenditures throughout the entire lifecycle. Not only does it speed up the development and deployment process, but it also generates efficiencies that improve control over infrastructure costs.
Perhaps the best way to understand these benefits is by seeing them in a real-world context. Below are two use cases in which ModelOps solutions can help enterprises accelerate, scale, and optimize analytics projects:
Finance
Sophisticated AI and machine learning operations have enabled major financial institutions to make strong, informed, and unbiased decisions through real-time analytics. This is especially helpful in mitigating fraud and money-laundering schemes, as is their legal obligation. ModelOps solutions can help banks improve such models by simplifying and automating the deployment cycle.
Take Teradata’s ClearScape AnalyticsTM platform, for example. A global bank uses the solution to prevent fraud, improve the customer experience, reduce losses, and increase business efficiency. Using ClearScape Analytics, the bank streamlined data preparation by 200 times, allowing its solution to go from fraud detection to real-time prevention.
Healthcare
AI and machine learning operations are making waves in the healthcare sector, particularly with the introduction of predictive analytics to numerous business functions. From diagnostics to patient experience, the applications are practically endless.
One major U.S. healthcare institution leverages ClearScape Analytics to expedite deployment and uplift patient personalization. Using the platform’s ModelOps capabilities, the company increased deployment productivity threefold, successfully rolling out 30 models. These tools predict which of the institution’s patients most likely need an office visit, allowing it to personalize the customer experience at scale.
Optimize ModelOps With VantageCloud and ClearScape Analytics
Reversing the years-long trend of failed AI projects won’t be easy. Implementing the right data architecture is a step in the right direction, but the best way to improve your effort by leaps and bounds is by leveraging a cloud-native data and analytics platform for AI like Teradata VantageCloud.
VantageCloud, in tandem with the ClearScape Analytics AI/ML engine, provides all the ModelOps capabilities you need to activate AI investments at scale. It addresses key challenges, such as end-to-end lifecycle management, deployment, model monitoring, and more.
With built-in governance tools—such as code tracking and automated model performance alerts—it supplies the ideal framework to manage, launch, and maintain your AI initiatives for continuous innovation. Deploy thousands of models in short order by processing and integrating data in the cloud, making it easily accessible for automated analytics models.
Ready to learn more? Get in touch to discover how Teradata VantageCloud can help your organization harness the power of artificial intelligence.