Enterprise Crop Classification with Snowflake Model Registry

In this project, you will build an end-to-end machine learning pipeline using Snowflake Model Registry to train, version, deploy, and serve a crop classification model in a production environment. You'll work with realistic agricultural datasets to solve a real business problem—automatically classifying crop types based on physical measurements to help farmers optimize resource allocation and increase yield.

Each phase is structured with clear objectives, hands-on tasks, and production-ready code you'll execute yourself. You'll start by setting up your local development environment with Python and required ML libraries, progress to configuring Snowflake infrastructure with proper security and governance, and finally deploy a scikit-learn Random Forest model that can be queried via SQL by non-technical users.

The project emphasizes real-world enterprise practices: environment isolation with virtual environments, version control of models, role-based access control, SQL-based model serving for business users, and cost-optimized compute resources. By the end, you'll have a complete understanding of how data scientists train models locally and deploy them to Snowflake for organization-wide consumption—bridging the gap between ML development and production operations.