AI weather and Earth‑modeling platforms deliver end‑to‑end pipelines for ingesting remote‑sensing and in‑situ observations, training machine‑learning and hybrid physics‑ML models, and serving operational forecasts and risk analytics. NVIDIA Earth‑2 typifies this class: a high‑performance, GPU‑centric stack aimed at scaling large‑domain simulations and real‑time inference. Competing solutions span cloud hyperscalers, research centers and specialist vendors that combine data management, model training, deployment and visualization. As of early 2026 the field is driven by three converging trends: far greater volumes of satellite and IoT data, the maturation of hybrid physics+ML modeling approaches that improve local accuracy, and demand for operational, near‑real‑time products for energy, insurance, logistics and emergency response. Those trends increase pressure on data platforms to provide scalable ETL, labeling, model‑ops and embedded analytics rather than point tools. Tool roles in this ecosystem: Labelbox and similar data‑factory platforms provide annotation, quality‑control and managed labeling services necessary to build supervised datasets from imagery and sensor streams. Domo and other data platforms connect diverse sources, automate data preparation and deliver real‑time dashboards and alerts for operational teams. Sisense and embedded‑BI vendors package model outputs into interactive analytics, white‑label dashboards and developer SDKs so domain users can integrate forecasts into workflows. Evaluations should consider data‑ingest breadth, model reproducibility, compute economics, latency for inference, labeling and QA workflows, and integration into enterprise analytics. The result is a practical, pipeline‑centric view of how NVIDIA Earth‑2 and its competitors serve applied weather, climate and Earth‑system use cases.