Harnessing GenAI to Optimize Hybrid Cloud Workloads
summary
Claims concrete wins: generative models drive predictive scaling that yields ~35% infrastructure cost reduction, analyze logs to trigger self‑healing and automated remediation that cuts manual interventions by up to 80%, generate synthetic attack scenarios for continuous hardening, and optimize networking to reduce latency by up to 40%. Implementation uses IaC patterns with Ansible, Kubernetes and Terraform, shows a bank saving ~$15M and a manufacturer reaching 99.99% uptime, and surfaces data‑quality and rollout steps for incremental adoption.
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chapters (12)
- 0:00Introduction to Generative AI in Hybrid Cloud
- 0:53Understanding Hybrid Cloud Complexity
- 1:49Challenges of Traditional Management Approaches
- 3:29AI-Powered Predictive Workload Management
- 4:38Enhancing Security with Generative AI
- 5:47Adaptive Network Optimization
- 6:52Automated Decision Making and Continuous Optimization
- 8:43Business Impacts of Generative AI
- 9:42Real-World Success Stories
- 10:34Challenges and Solutions in Implementation
- 11:47Future of Generative AI in Hybrid Cloud and Edge
- 13:33Conclusion
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