ML Jobs in Snowflake Data Clean Rooms Now GA
| Source: Snowflake Blog
Tags: Snowflake, data clean rooms, federated ML, privacy-preserving ML, enterprise ML, distributed training, VideoAmp
Snowflake's ML Jobs for Data Clean Rooms is now generally available, enabling enterprises to run distributed GPU-accelerated ML workloads across multi-party datasets without any organization's raw data leaving its own account.
Details
Snowflake has moved ML Jobs in Data Clean Rooms to general availability, removing a longstanding constraint: clean room environments were previously limited to SQL or single-node Python, which hit memory ceilings at enterprise data volumes. Teams end up treating clean rooms as compliance theater rather than production ML infrastructure. The GA release lets data scientists bring their full Python ML stack — distributed training, hyperparameter optimization, custom packages, GPU compute — directly into multiparty collaborations. Models train on combined signals from multiple organizations simultaneously, while raw records remain inside each contributor's account and proprietary model code stays invisible to data partners. The flagship use case is digital advertising: an advertiser trains audience and measurement models by combining publisher ad logs, identity provider data, and retail transaction signals in a single run. VideoAmp, an early customer, reports processing lift reports 'faster, using more signals than ever before.' Affinity Solutions is also cited as a production user. Snowflake explicitly positions this as the foundation for multi-org AI agent fine-tuning — an infrastructure play ahead of what it expects will be the next wave: training agents on behavioral signals distributed across organizational boundaries inside governed clean room environments.