What happens when a GeoAI workflow needs to run reliably on new imagery every week, across different seasons and geographies, without someone manually checking every output?

This workshop introduces MLOps thinking to the EO community: the engineering practices that take a working pipeline and make it robust, reproducible, and production-aware. Through a hands-on session, using as an example a Maritime Surveillance problem, participants will explore how to validate inputs, track experiments, test outputs, and detect when something silently breaks. No cloud accounts or infrastructure setup required.

Learning Outcomes
- Understand the key principles of MLOps and how they apply to EO workflows
- Be able to identify common failure modes when moving from experimentation to production
- Apply basic input validation, experiment tracking, and output testing to their own pipelines

Prerequisites
- A Github account, with the ability to run a Github Codespace, OR the ability to run code locally in a laptop, including Docker containers.
- Familiarity with EO data concepts (bands, resolution, indices) is helpful but not required
- Some experience with Python