We will be bringing our expertise in software development and MLOps to deploy cutting-edge ML models for the sciences.
Why?
While ML has enabled significant progress towards grand challenges in science, rarely are Machine Learning (ML) projects packaged in a way where scientists/non-ML specialists can easily pick up advanced workflows. Similarly, ML engineers are not always able to contribute meaningfully to a science domain without being provided with useful application context or analysis-ready data.
