AstraZeneca: Reproducible AI & Governance in Healthcare

This Domino page addresses the general challenge regulated healthcare organizations face implementing AI: reproducibility, compliance, and governance complexity. AstraZeneca partnered with Domino to build AI infrastructure around 'scientific units of work' that bind data, environment and logic into immutable, auditable packages, including a model-benchmarking platform for clinical tasks, an AI Gateway with risk-based access to approved models, self-serve compute/publishing, and a patient-data agent for clinical trial workflows. The patient-data agent reached production 'in weeks because the environment was already qualified, approved for that data classification, and wired into audit infrastructure,' and a generative app was 'built and deployed in a single afternoon, with no IT ticket and project overhead.' The core message is treating governance as infrastructure rather than friction.

Domino Data Lab is an enterprise MLOps platform built for regulated industries — the sanctioned sandbox where biopharma teams run reproducible experiments under GxP/21 CFR Part 11 guardrails.

This sits in the Design & Stress-Test stage of the AI use-case lifecycle: Named examples from the tools pharma teams use to scope, prototype and benchmark an AI use case before committing to build it — data-catalog, sandbox and evaluation platforms.