Case Study: Ecolab achieves rapid, scalable production ML deployment and cost savings with Iguazio

Hygiene technologies leader Ecolab brings data science to production with Microsoft Azure and Iguazio

Ecolab, the global leader in water, hygiene, and infection prevention solutions, needed to turn sensor data into sophisticated predictive risk models but struggled with long, fragmented machine-learning deployment cycles and a geographically dispersed team. Already invested in Microsoft Azure, Ecolab engaged Iguazio for MLOps expertise to unify development, scale compute efficiently, and accelerate productionizing AI across its organization.

Iguazio delivered an MLOps platform (built on open-source components like Kubeflow) integrated with Azure, Azure DevOps, Git, and Azure Pipelines to automate feature engineering, CI/CD, and model deployment. The solution cut model deployment timelines from more than 12 months to 30–90 days by 2020, reduced compute provisioning from days to minutes, doubled expected platform users to over 40, supported 3–4× more models than planned, and lowered costs through shared compute—demonstrating Iguazio’s measurable impact on Ecolab’s AI rollout.

Ecolab

Craig Senese

Senior Director of Advanced Analytics