There’s never been a more exciting time to work in networking and networked applications. This talk will examine how machine learning (ML) benefits networking by focusing on four examples. First, we’ll examine for Intent-Based Networking (a modern architecture for designing and operating a network) and how ML can be used to increase visibility, diagnose problems and identify associated remedies, and provide assurance that the network is operating as intended. Next, we’ll look at how the move from today’s Cloud-based ML to the promising approach of Distributed ML across Edge and Cloud can lead to improved scalability, reduced latency, and improved privacy. We’ll also discuss how to identify what devices are on the network and how the network should treat those devices. Lastly, in the context of ever-growing security threats, we examine how ML can be applied to address the challenge of malware sneaking in an encrypted flow. Specifically, how we can detect malware hidden in encrypted flows without requiring decryption of those flows. It is noteworthy that while ML is often associated with reducing privacy, this example showcases how an elegant application of ML can both preserve privacy and reduce complexity.