MLOps

MLOps is the discipline of applying DevOps principles to machine learning workflows, encompassing the entire lifecycle from data preparation and model training through deployment, monitoring, and retraining. It establishes practices, tools, and organizational patterns that enable teams to reliably and efficiently develop, deploy, and maintain ML systems in production. MLOps addresses the unique challenges of ML systems that depend on both code and data, where model performance can degrade silently as input data distributions shift over time. MLOps for Geospatial AI PipelinesGeospatial ML systems have specific MLOps requirements. Data versioning must track not just tabular datasets but large satellite imagerySatellite ImagerySatellite imagery consists of photographs and data captured by Earth observation satellites orbiting the planet. Thes... archives, including sensor metadata, acquisition dates, and geographic coverage. Experiment tracking records which imagery, preprocessing parameters, model architecture, and hyperparameters produced each model version. Automated retraining pipelines update models as new satellite imagery or ground truthGround TruthGround truth refers to data collected at the Earth's surface to validate and calibrate information derived from remot... data becomes available. Geographic performance monitoring tracks model accuracy across different regions, detecting when models underperform in specific areas. Feature stores manage spatial features like proximity metrics, demographic indicators, and terrain attributes that are shared across multiple geospatial models. Key Practices and Tooling for Production Geospatial AICI/CD (Continuous Integration/Continuous Deployment) pipelines automate the testing and deployment of model updates. Model registries track all model versions with their training provenance and performance metrics. A/B testing enables gradual rollout of model updates with geographic stratification to ensure new models improve performance across all regions. Monitoring dashboards track inference latency, prediction distributions, and accuracy metrics against ground truth when available. Popular MLOps platforms like MLflow, Kubeflow, and Weights & Biases are increasingly adopted for geospatial AI workflows, with emerging platforms specifically designed for Earth observation model management.