Vian H+AI Platform
Monitoring and Continuous Operations
Unlike traditional software applications that remain relatively static once they are deployed, machine-learning (ML) models drift – that is, their behavior changes when they are running in production environments because the real-world data they are being fed differs from the data with which they were trained.
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The Vian H+AI Platform was designed to solve these problems by streamlining the workflow for bringing models to production and simplifying and automating continuous monitoring and model management. Its open, modular design makes it easy to monitor models built in other platforms and tools, and provides users advanced deployment techniques and rich, real-time metrics that help eliminate alert fatigue.
Vian H+AI Platform | Human-Centered AI
Our platform was built to monitor machine-learning models at a massive scale. Because the platform can handle such high volumes of data, it is critical for the platform to address bias and fairness, and ultimately make the data actionable.
With easy-to-digest graphs and models, the platform is designed to support the people behind the models, empower them to make insightful decisions, and monitor models in real-time, to ensure they are still doing what they are supposed to. This is part of our human-centric focus, and we also think it’s foundational to building trusted, scalable AI systems that are both high-performing and cost-effective.
Most models running on edge devices gather large amounts of data that is typically processed, analyzed and interpreted in a cloud or on-premises data store, delaying the ability to inform users of anomalies or problems in time to act. With our platform, users can monitor models at the edge, closer to where data is processed to perform inferencing and provides users real-time access to information so they can respond more quickly.
Monitoring and Continuous Operations
Monitoring ML models in production is an essential step in ensuring that your models are performing as expected. That’s why we built a comprehensive, automated approach to model monitoring with real-time metrics to know when and why a model’s behavior has changed, and when necessary, execute an automated workflow to retrain and redeploy.
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Wizard-driven policy creation lets users define risk monitoring policies for each model. Flexibility and customization help eliminate alert fatigue by ensuring only critical notifications are sent to users with key information to understand the issue. Monitoring policies can be either distance or window based, with support to view billions of data points at once.
When monitoring a high volume of models in production, it is difficult to keep track of multiple model versions, sift through multiple alerts, and identify the best performing models to use when it is time to replace production models. Our platform helps eliminate alert fatigue by providing multiple ways to view model performance based on your own pre-defined criteria, and lets you set up alerts to show you when critical thresholds are met – with the information you need to make decisions.
Support for advanced deployment options including Canary & Shadow and Champion & Challenger allow you to monitor and test performance and automate model replacement when a new model is performing better than the one in production – all while eliminating disruption to business operations.
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