Operations | Monitoring | ITSM | DevOps | Cloud

Network management teams are drowning in alerts, tickets, and manual escalations—slowing MTTR, driving up costs, and jeopardizing service quality. But the rise of AI-driven automation is changing the game. In this webinar replay, experts from Grokstream and Resolve will discuss how AIOps and automation are shaping the future of autonomous networks. Join Josh Kindiger and Ari Stowe to see firsthand how AI can reduce noise, detect root causes, and trigger end-to-end remediation without manual effort.

Spike.sh is a modern incident response platform that brings every alert into one place—phone calls, SMS, Slack, Teams, WhatsApp, Telegram, and more. It auto-generates clear incident titles, detects severity in real time, and logs every action so your team can move from alert to resolution faster. In this video, you’ll see how to.

Join Jimil Patel, Head of Technical Product Marketing and Developer Advocacy at Intuit, as he shares the company's transformative journey from cloud-native to AI-native, resulting in a 6x increase in developer velocity. Recorded at Civo Navigate Austin 2025, this talk explores Intuit's Modern SaaS AIR platform, AI-powered developer tools, intelligent auto-scaling, and AIOps-driven operations. Discover how Intuit is redefining the future of software development with real-world examples, including IKS AIR for self-healing runtimes and AI-driven observability, which cut MTTR by 50%.

In this episode, Itiel has an insightful conversation with Dr. David Morrison, a research scientist and founder specializing in Kubernetes scheduling and autoscaling. David shares his journey from operations research to leading distributed systems efforts at tech giants like Yelp and Airbnb. Learn about the transition from Apache Mesos to Kubernetes at Yelp, including the role of their open-source API layer, Pasta.

As organizations expand their Kubernetes deployments and scale production workloads, effective cost management becomes an essential priority. The rapid innovation demanded from development teams often intersects with a shortage of advanced Kubernetes expertise, leading to resource inefficiencies and unnecessary expenses. This challenge is further amplified by the growing prevalence of AI/ML workloads and the intricate demands of GPU utilization.