This topic covers the infrastructure and integrations that enable scalable, secure cloud AI compute and high‑performance model training—bringing together cloud platform connectors, Kubernetes/OpenShift tooling, and data‑pipeline orchestration. By 2026, production AI workloads increasingly require not just raw GPU/accelerator capacity but reproducible deployment paths, safe code execution, and standard programmatic interfaces for LLMs and orchestration systems. Key components include Model Context Protocol (MCP) servers that let AI tools manage cloud resources: Google Cloud Run MCP deploys apps to Cloud Run; an AWS MCP exposes S3 and DynamoDB operations; a Kubernetes/OpenShift native MCP server provides direct CRUD against cluster resources without external dependencies. Dagster’s MCP server connects pipeline orchestration to AI workflows, while Pinecone’s MCP server links vector database projects to assistants. Daytona adds a security layer by running AI‑generated code in isolated sandboxes to limit blast radius during automated tasks. Together these tools address pressing operational needs: automating model deployment and dataset flows, integrating vector search and storage, enforcing secure runtime boundaries for generated code, and providing Kubernetes‑native management for scalable training clusters. Trends driving relevance include the growth of large multimodal models, distributed training across heterogeneous accelerators, demand for auditability and isolation in AI‑driven automation, and the emergence of protocol standards (MCP) that let LLMs interface consistently with cloud and orchestration layers. This topic is practical for engineering teams evaluating vendor integrations, platform architects designing secure training stacks, and SREs seeking reproducible pipelines that combine cloud compute, Kubernetes control, and pipeline orchestration.