The do's and don'ts: GenAI applied to infrastructure скачать в хорошем качестве

The do's and don'ts: GenAI applied to infrastructure

This presentation explores the potential of LLMs & GenAI in infrastructure management, focusing on current capabilities, limitations, and future developments. 1. LLM Models: I'll explain how LLMs work, how they are trained, and their probabilistic approach. I'll illustrate difference through Python & Terraform code generation comparisons. 2. Issues with IaC Generation: Context Limitations: AI might produce suboptimal configurations due to missing context, leading to inconsistencies or dependency issues. Security Risks: Models trained on public data may propagate vulnerabilities (e.g., open ports) and bad practices. 3. Synthesis vs. Generative AI: Generative AI creates code/content, while synthesis AI analyzes and combines existing information, like logs, to identify issues. Understanding this distinction is crucial for effective use. 4. Future Potential - Contextual AI: AI’s full potential will be realized when it integrates comprehensive environmental context, including configurations, service interdependencies, and security policies. Speaker: Roxane Fischer Subscribe to our YouTube Channel → https://www.youtube.com/c/HashiCorp?s... For hands-on interactive labs, visit HashiCorp Developer → https://developer.hashicorp.com/ HashiCorp provides infrastructure automation software for multi-cloud environments, enabling enterprises to unlock a common cloud operating model to provision, secure, connect, and run any application on any infrastructure. HashiCorp tools Vagrant, Packer, Terraform, Vault, Consul, Nomad, Boundary, and Waypoint allow organizations to deliver applications faster by helping enterprises transition from manual processes and ITIL practices to self-service automation and DevOps practices. For more information → https://hashicorp.com LinkedIn →   / hashicorp   X → https://x.com/HashiCorp Facebook →   / hashicorp