Many organizations using Spark for machine learning (ML) need to move data from storage to an external environment, which can be difficult to scale and costly to maintain. This is often complicated by the manual management and cluster tuning required in these legacy processing environments. To streamline end-to-end ML workflows on a single, governed platform, Snowflake ML is the integrated set of capabilities for model development and operations.
Join us for a session with Snowflake experts on migrating ML workloads from Spark ML to Snowpark ML and learn about:
- An overview and comparison of Snowflake ML vs. Spark ML
- The advantages of a unified platform in Snowflake for the full ML lifecycle
- Considerations and best practices for Spark to Snowflake ML migrations
Speakers
Simran Khara
Architect, Machine Learning Field CTO
Snowflake
Lucy Zhu
Product Marketing Manager
Snowflake