Model Deployment

Model Deployment is the engineering process of taking a trained machine learning model from a development environment and making it available for real-world use. This involves packaging the model with its dependencies, setting up serving infrastructure, creating APIs for input and output, implementing monitoring and logging, and establishing update and rollback procedures. Deployment transforms a research artifact into an operational system that delivers value by processing new data and producing predictions reliably and at scale. Deployment Patterns for Geospatial AI SystemsGeospatial AI models are deployed through several patterns depending on the application requirements. Batch processing pipelines run models on new satellite imagerySatellite ImagerySatellite imagery consists of photographs and data captured by Earth observation satellites orbiting the planet. Thes... as it becomes available, producing updated land cover maps, change detectionChange DetectionChange detection uses geospatial data and imagery to track and analyze alterations in landscapes, infrastructure, or ... results, or feature inventories on regular schedules. Real-time inference APIs serve predictions on demand, enabling interactive geospatial applications like on-the-fly classification of user-uploaded imagery. Edge deployment places models on drones, satellites, or field devices for low-latency, connectivity-independent analysis. Streaming deployments process continuous data from IoT sensors for real-time environmental monitoringEnvironmental MonitoringEnvironmental Monitoring is the systematic collection and analysis of data about environmental conditions, including .... Serverless deployment scales automatically with demand, which is useful for variable workloads like disaster response where analysis demand spikes unpredictably. Challenges Specific to Geospatial DeploymentGeospatial model deployment faces unique challenges. Satellite imagery inputs are extremely large, requiring tiled processing and output stitching to handle full scenes. Multi-spectral and temporal inputs need preprocessing pipelines that handle different sensor formats and coordinate systemsCoordinate SystemsCoordinate systems standardize the description of geographic locations using latitude, longitude, and other spatial d.... Model performance must be monitored for geographic drift, where accuracy degrades as models encounter regions or conditions different from their training data. Versioning must track not just model weights but also the preprocessing logic, geographic training extent, and input sensor specifications that affect predictions.