Successfully operationalizing machine learning (ML) is critical for organizations seeking to maximize the return on their initial investments. Building ML solutions in Snowflake requires not only architectural choices but also careful consideration of how to seamlessly integrate all components across the ML lifecycle. This session explores key trade-offs, lessons learned and other factors to effectively operationalize ML in Snowflake.