The Challenge
Beyond Weather faced the classic research-to-production bottleneck that affects many climate tech companies emerging from academic settings. Their algorithms worked brilliantly in research environments but weren’t operationalized for commercial use. The technical complexity was immense – processing ERA5 datasets with hourly data back to 1940, spatial resolution of 31km, and 137 vertical atmospheric levels up to 80km altitude. This required infrastructure thinking at the chip level to handle the computational demands, while simultaneously building the MLOps framework needed for real-time inference and model iteration.
Our Approach
We embedded with Beyond Weather’s team through a design sprint to architect their production infrastructure strategy.
Research-to-Production Bridge
- Transformed academic algorithms into commercial-grade systems designed for continuous deployment and real-time inference
- Implemented MLOps frameworks that enabled rapid model iteration beyond the static research environment
Infrastructure Architecture Assessment
- Evaluated bare metal server approaches for maximum computational control given the massive dataset complexity
- Designed scalable compute strategies to handle ERA5’s 60+ years of multi-dimensional atmospheric data
Productization Framework
- Built systems thinking around operational reliability rather than one-off research outputs
- Established deployment pipelines that could support energy trading and agricultural decision-making timelines
