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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.
I’d like to send a huge thanks to our friends at Juniper Networks for sponsoring today’s show. Juniper is a leader in AI-Native Networking, helping IT teams at companies like Gap, ServiceNow, and Verizon, simplify network operations and make every connection count. Powered by Mist AI, Juniper delivers industry-leading AIOps, providing end-to-end insight into user experiences and proactive anomaly detection. Using a combination of network and application data, along with custom-built ML and AI models, Juniper Mist can do things like pinpoint the network issues impacting Zoom calls—because choppy calls are the worst. See a demo at juniper.net/twiml.
