Gen AI Workflows for Climate Risk Modeling in Food Systems
summary
ERA5 provides 31 km global analysis since 1940 and Sentinel‑2 supplies 10–20 m multispectral revisits; CMIP6 defines SSP futures. GraphCast GNN claims >90% targets beating ECMWF, weather 3D transformer gives orders‑of‑magnitude speedups, and a neural GCM runs stable multi‑decade forecasts. Cardiff downscaling and CORDEX‑style methods produce farm‑level precipitation and temperature fields; CI/CD, Kubernetes, Azure ML/SageMaker registries, Evidently and Prometheus enable canary rollouts, drift detection and traceable synthetic extremes and automated LLM briefs for farmers.
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chapters (7)
- 0:00Introduction to Sustainability and Climate Risk Modeling
- 0:53The Impact of Climate Change on Food Systems
- 1:38Climate Data and Generative AI for Forecasting
- 2:59DevOps and Climate Scenario Building
- 4:49Core Capabilities for Climate Resilience
- 6:14DevOps Integration and Practical Implementation
- 8:23Governance, Safety, and Key Takeaways
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