Reproducible workflows on phiz

Making CCHS analysis faster and easier to repeat

Mon, 13 Jul 2026 00:00:00 -0400

Public health teams spend an appreciable amount of time responding to requests to analyze CCHS data. These requests often require complex code, and even small changes in scope can mean updating the analysis workflow. As a result, some opportunities to use CCHS data are missed. The work is time-intensive, and it often depends on a small number of experts who understand how to apply the required weighting and bootstrap techniques correctly.

At WDG Public Health, we are reimagining the CCHS analytics process by creating an automated way to analyze the data while maintaining the same technical accuracy. An automated approach would reduce the time required for routine requests and allow teams to reallocate that time to other priorities. The goal is to make the process easier to use, easier to share, and easier to improve through collaboration and an open approach to development.

CI/CD: Its Place in Public Health Software Teams

Mon, 04 May 2026 00:00:00 -0400

Python GIS: Advantages and Integration

Fri, 06 Mar 2026 00:00:00 -0500

MLOps in Public Health: Workspaces, Pipelines, and Governance

Fri, 20 Feb 2026 00:00:00 -0500

Modern data science and ML work depends on consistent environments, controlled access patterns, and reproducible workflows. In public health, these needs underpin governance as much as they support productivity.

At Wellington-Dufferin-Guelph Public Health (WDGPH), we have found that the simplest way to address both delivery and risk is to standardize how tools are delivered into managed, isolated environments, and to route repeatable work through governed pipelines. This post focuses on those two foundations, developer environments and pipelines, within a larger MLOps program.