Vino Duraisamy - From raw data to interactive data app in an hour | PyData Global 2023

Snowpark enables end-to-end ML workflows within Snowflake by allowing Python, Java and Scala code execution alongside SQL

Key components include DataFrame API for data engineering and ML Modeling API for machine learning tasks, removing need for external compute environments

Model Registry provides versioning and deployment capabilities for ML models, with metadata tracking and easy deployment options

Zero-copy cloning allows multiple teams to work on the same data without creating redundant copies, maintaining data governance

Pre-processing and feature engineering can be done using familiar APIs similar to scikit-learn (ordinal encoding, one-hot encoding, scaling etc.)

Supports popular ML libraries like scikit-learn, XGBoost while optimizing execution through Snowflake’s compute engine

Eliminates data silos by keeping entire workflow within Snowflake’s security boundary instead of moving data between environments

Model deployment is simplified to a two-step process: logging the model and deploying it for inference

Provides standardized environments for both development and production through container services and GPU-enabled compute options

Interactive visualization apps can be built using Streamlit integration for model monitoring and results display