As AI technologies continue to advance, data center operations are undergoing significant transformations. AI-driven solutions enhance efficiency, reliability, and scalability in managing data centers. Underpinning this are the new challenges and opportunities presented by AI workloads, including machine learning and deep learning, as these require significant computational resources and generate massive amounts of data. Join me for a network-centric view of where we are heading with these exciting new technologies. Learn more at Introduction and Overview – 00:00 Future of Interface Speeds – 00:33 Explaining 1.6 Terabit Speeds – 01:07 Speed Analogy: Draining Lake Mead – 01:38 Operationalizing AI in Data Centers – 02:40 Defining AI, Analytics, and Automation – 03:11 Evolution to Autonomous Networks – 03:42 The 5 Pillars: Self-Organizing to Self-Remediating – 04:13 Real-World Use Cases for AI in Networking – 06:16 Self-Provisioning Networks – 07:19 Importance of Self-Monitoring – 07:50 Self-Healing Networks – 08:51 Predictive Failures and Healing – 09:52 Self-Securing and East-West Traffic Challenges – 10:23 Data Visibility and the Role of DPUs – 11:26 Inline Monitoring and Avoiding Data Center Bloat – 11:56 Critical Vulnerabilities and Patch Challenges – 13:28 Automation for Vulnerability Management – 14:33 AI for Security Policy Enforcement – 16:08 Real-Time Monitoring and Policy Automation – 16:39 Telemetry and Network Data Visibility – 17:10 Need for DPUs in Future Data Centers – 17:40 Data Lakes, Challenges, and Cloud Options – 18:12 Security Risks in Faster Environments – 18:44 Trust Issues in AI-Powered Networks – 19:15 Final Thoughts and Future Outlook – 19:46