Production AI

Cloud Security Challenges in the AI Era: Analyzing the Impact of Containers and Inference on System Safety

Production AI

Cloud Security Challenges in the AI Era: Analyzing the Impact of Containers and Inference on System Safety

Overview Marina Moore — security researcher and co-chair of the CNCF Security and Compliance TAG — shares her concerns about security vulnerabilities inherent in container-based architectures. She outlines: * Origins of the issues * Potential solutions * Alternatives such as micro‑VMs instead of traditional containers * Special risks around AI inference workloads --- 🔑 Key

Production AI

Reducing False Positives in RAG Semantic Caching: A Banking Case Study

## Key Takeaways - **Semantic caching** — a **Retrieval-Augmented Generation (RAG)** technique — stores queries and responses as **vector embeddings** for reuse. - Improves **efficiency** by avoiding repeated large language model (LLM) calls. - Case study: failure ➡ production success through **7 bi-encoder models**, **4 setups**, **1,000 banking queries**. - **Three model types** tested: compact,

Production AI

KubeCon NA 2025 - Salesforce’s AIOps and Intelligent Agent Approach to Self-Healing Practices

AIOps & Agentic AI for Self-Healing Kubernetes Platforms AIOps and Agentic AI technologies enable intelligent assessment of Kubernetes cluster health, automatic issue diagnosis, and orchestrated resolutions with minimal human intervention. At KubeCon + CloudNativeCon North America 2025, Vikram Venkataraman (AWS) and Srikanth Rajan (Salesforce) presented Salesforce’s approach to building