Gen AI applications often rely on Kubernetes for orchestration, utilizing a cloud-native, distributed microservices architecture. The high cost and scarcity of GPUs makes any downtime or idle capacity detrimental and wasteful. Prompt identification of breakdowns and idle capacities is essential. Additionally, these applications handle confidential data and training services from outside the cluster, necessitating secure workload egress traffic.

In this webinar, we will explore how to optimize and secure Gen AI applications running on Kubernetes. Key topics include:

• Monitoring network traffic for workload performance and communication issues.
• Utilizing purpose-built observability tools to troubleshoot connectivity and performance issues.
• Visualizing dependencies and live workload communication with dynamic service and threat graphs.
• Detecting and responding to anomalous behaviors for quick remedial action.
• Securing egress access and preventing lateral movement of threats.