Organizations across industries are turning to machine learning (ML) to derive the greatest impact from their data, and maximize new opportunities that can set them apart from their competitors. However, businesses must develop a process that helps push their ML models into production and deployment while ensuring quality and consistent monitoring.
ML model development and deployment are inherently challenging. According to a recent survey, it generally takes anywhere between about 30 to 90 days to push an individual ML model into production, and a year or more on productionizing. Even so, it’s estimated that around 90 percent of all ML models fail to even make it to production. Also, the time and resources dedicated to bringing a model into deployment typically costs organizations a significant amount of money and effort.
This is where ModelOps, or artificial intelligence model operationalization, can bring a lot of value to organizations. ModelOps streamlines the process of bringing models into production while ensuring quality performance, scaling, and monitoring.
From a business standpoint, organizations are making considerable investments in artificial intelligence (AI) and ModelOps helps to maximize those AI investments by optimizing models across the entire lifecycle. Organizations need models to be scalable and accurate, and monitoring ML models ensures higher performance and results.
Developing a ModelOps Process
Businesses must implement a process of ModelOps best practices that defines a set of criteria for evaluating the cycle and performance of ML models. For example, ModelOps aids in development, deployment, and testing of ML models so that businesses can verify the effectiveness and quality of their models for real-world use cases.
Not to be confused with MLOPs, which targets the operationalization of ML models, ModelOps is used to target the operationalization of all analytics, AI and decision models. MLOps is also considered to be a factor in data sciences platforms vs. ModelOps, which is an enterprise capability.
Organizations need to build, manage, and measure their models and be prepared to continuously update and refine those models over time, including providing application functionality in the cloud. This requires the capabilities to automate data preparation, provide a level of explainability into their models, and evaluate possible bias. ModelOps can even aid in the development and deployment of more complex ML models.
That’s not all. Businesses also need to maintain all of their models with the capabilities for teams to track model progress, and ModelOps aids businesses in establishing a framework to enhance collaboration between teams and departments. That way people can continuously update, evaluate, and control models while bringing transparency into AI and analytics.
ModelOps enables reliable outcomes for analytics, AI and decision models so that businesses can make the best possible decisions once the models are running in production. ModelOps intersects with DevOps and MLops, bringing models from creation to production while providing continuous updates and retraining.
Another advantage of ModelOps is that it can support a more holistic approach to analytics model development. Models aren’t stagnant and are impacted by dynamic environments and the populations they’re intended to represent. They’re also impacted by continuous relationship changes, such as concept drift, data drift, and business drift. For example, data drift can impact the accuracy of a model’s predictions and is one of the main contributors to model degradation over time. But monitoring, evaluating and testing model behaviors and efficacy via ModelOps can ensure the viability and quality of the model’s performance.
Here’s a quick look at some other benefits of ModelOps:
- End-to-end visibility and auditability into model production
- Enhances and automates governance for AI and analytics models
- Automates the model lifecycle
- Opens up model deployment to business analysts
- Ensures model accountability and repeatability
- Accelerates AI project outcomes and time to value, and streamlines model deployment
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A New Outlook for AI Operationalization
Managing models demands significant oversight - from the data and predictions to governance and model accuracy. All of these factors can put additional pressures on data science resources. ModelOps can alleviate many of the challenges associated with operationaling models and empower organizations to accelerate their enterprise AI strategies while maximizing their analytics investments.
If you’re interested in learning more about ModelOps, check out Simplilearn's AI and ML Course course for additional resources and educational information.