Host Philip Gervasi talks with Uber's Vishnu Acharya about how Uber applies machine learning and MLOps to network operations at hyperscale. Vishnu explains Uber’s intentionally simple network design across on-prem and multi-cloud, then shares practical machine learning use cases like predictive capacity planning, hardware failure rate-tracking, and alert correlation to reduce noise and speed mitigation. They also discuss organizational issues, including building blended network/software teams, partnering with internal ML groups, and focusing on service-level outcomes over hype.

A new wave of civil unrest in Iran has triggered one of the most severe communications crackdowns the country has seen, with disruptions extending beyond the internet to voice, SMS, and Starlink. Host Philip Gervasi is joined by Amir Rashidi (Miaan Group / Filterwatch) and Kentik’s Doug Madory to break down how the shutdown unfolded, what the data shows as connectivity collapses, and why attribution is tricky in the fog of a rapidly evolving crisis. They discuss Iran’s National Information Network (the domestic “intranet”), how shutdown tactics have grown more targeted and sophisticated over time, and what makes this event different.

Host Philip Gervasi is joined by Andrés Azpúrua (Executive Director, VE sin Filtro / “Free and Secure Online”) and Kentik’s Doug Madory to examine what internet visibility can, and can’t, tell us during a fast-moving political crisis in Venezuela. They discuss Venezuela’s “baseline” reality of brittle infrastructure and recurring power outages, alongside long-standing, regulator-mandated censorship targeting independent media, social platforms, and even exchange-rate information. Using multiple data perspectives, the episode explores how traffic patterns, localized outages, and BGP noise can be misread without ground truth, and why careful attribution matters.