Predicting User Experience: Inside the Marvis Large Experience Model
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Predicting User Experience: Inside the Marvis Large Experience Model
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In this episode of The Q&AI Podcast, host Bob Friday is joined by Kumar, Product Manager for Marvis®, and Navraj, Director at Juniper Networks, to discuss how Juniper’s Marvis Large Experience Model (LEM) is revolutionizing the way enterprises monitor and troubleshoot collaboration application performance.
Originally designed to correlate Zoom and Teams data with network metrics, LEM has evolved into a generalized, AI-driven system that can predict poor user experiences—even without third-party labels. The conversation explores the model’s architecture, real-world customer use cases, and how its integration with tools like Marvis Minis is accelerating the shift toward intelligent, self-driving networks.
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Key points covered:
Targeted troubleshooting: LEM pinpoints whether collaboration app issues stem from the network, client, or app, — eliminating guesswork.
AI-driven predictions: Trained on massive datasets, LEM forecasts video/audio quality using Juniper-only telemetry, even without third-party labels.
Root cause clarity: Uses Shapley values to break down contributing factors like Wi-Fi, WAN, or client-side issues.
Actionable insights: Automatically recommends and applies fixes (e.g., RF tuning, 5 GHz shifting) with measurable impact.
Smarter networks: With Marvis Minis and switch integration, LEM enables proactive, self-driving network operations.
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Where to find Kumar?
LinkedIn - https://www.linkedin.com/in/kumar-putta-swamy/
Where to find Navraj?
LinkedIn - https://www.linkedin.com/in/navraj-pannu-746359177/
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Keywords
Marvis Large Experience Model, Customer value proposition, Collaboration apps, Cloud integration, User experience, Juniper MIST data, Predictive model, Shapley algorithm, Network features, Bad user minutes, Generalized model, Self-driving actions, WAN issues, Wireless capacity, Application Minis, Gen-AI conversational interface
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We hope you enjoyed this episode!