Today, we're joined by Shirley Wu, senior director of software engineering at Juniper Networks to discuss how machine learning and artificial intelligence are transforming network management. We explore various use cases where AI and ML are applied to enhance the quality, performance, and efficiency of networks across Juniper’s customers, including diagnosing cable degradation, proactive monitoring for coverage gaps, and real-time fault detection. We also dig into the complexities of integrating data science into networking, the trade-offs between traditional methods and ML-based solutions, the role of feature engineering and data in networking, the applicability of large language models, and Juniper’s approach to using smaller, specialized ML models to optimize speed, latency, and cost. Finally, Shirley shares some future directions for Juniper Mist such as proactive network testing and end-user self-service.
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📖 CHAPTERS
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00:00 - Introduction
5:07 - Time series and networking for ML&AI
10:17 - Layers in optimizing wi-fi connectivity
17:36 - Transformer-based models for time series
23:19 - Mist
24:50 - Marvis
29:18 - LanGraph integration
31:35 - RAG
34:19 - Use cases
41:19 - Small models
44:15 - Traditional vs AI/ML methods
46:56 - Future directions
🔗 LINKS & RESOURCES
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On-Demand: From Reactive to Proactive Networking with Mist AI - https://www.juniper.net/twiml
Learn more about Juniper AI - https://juni.pr/3YWhq5b
Marvis Virtual Network Assistant - https://www.juniper.net/us/en/products/cloud-services/marvis-virtual-network-assistant.html
Explainable AI: Understand the logic behind Juniper’s Mist AI - https://www.juniper.net/us/en/dm/explainable-ai.html
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