ML Jobs in Snowflake Data Clean Rooms Now GA

ML Jobs in Snowflake Data Clean Rooms Now GA

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Blog / AI & ML / Introducing ML Jobs in Snowflake Data Clean Rooms: Collaborative Training and Scoring Across Multiparty Data

JUL 08, 2026 / 11 min read AI & ML Introducing ML Jobs in Snowflake Data Clean Rooms: Collaborative Training and Scoring Across Multiparty Data

Ashwin Chandra Putta

Until now, running complex ML workloads inside a data clean room meant hitting a wall. Most clean room environments are limited to SQL queries or single-node Python, which runs into memory constraints quickly at enterprise data volumes. Teams ended up treating clean rooms as a compliance tool rather than a place to build models. ML Jobs changes that. ML Jobs in Snowflake Data Clean Rooms™ is now generally available.

Data scientists can now bring their standard Python ML stack with distributed training, hyperparameter optimization, custom packages and GPU compute directly into a multiparty collaboration. Models train on combined data from multiple organizations without raw records leaving anyone's account, and the pipeline runs automatically rather than requiring manual intervention each time.

Consider a concrete example from advertising. An advertiser builds audience and measurement models using ad log data from publishers, identity resolution data from identity providers and transaction signals from retail data partners. Each data source improves model quality. However, each provider has legitimate concerns about how their data is used and who can see it. At the same time, the advertiser's model logic and scoring algorithms are proprietary IP they have no interest in exposing to their data partners.

ML Jobs in Data Clean Rooms gives each party what they need. Data and identity providers know their data is governed and used only for workloads they have explicitly approved. The advertiser's model code stays inside the collaboration boundary, invisible to the data providers. As new data flows in, models score continuously on fresh signals without any party renegotiating access or re-exposing their assets.

The same infrastructure also points toward what's next: training AI agents whose effectiveness depends on signals distributed across multiple organizations. This includes a publisher's behavioral data, an advertiser's conversion history, a data provider's demographic enrichment, all combined in a single fine-tuning run inside a governed clean room. That's where multiparty ML is heading, and the foundation is available today.

“Snowflake's ML Jobs release in DCR enabled us to bring VideoAmp's best-in-class lift methodology into a dramatically more scalable computing environment. We can now process lift reports faster, using more signals than ever before — unlocking greater value for our clients at scale.” —Katy Mitchell, Sr Director Ad Measurement Products, VideoAmp

"At Affinity Solutions, we're proud to use ML Jobs in Snowflake Data Clean Rooms. For advertisers, the ability to train and score models on combined data without raw records leaving anyone's environment solves a problem that has blocked serious measurement and targeting work for years. What excites us most is the foundation this creates for what's next: AI agents continuously trained on our consumer purchase data and our clients' data in a safe, secure and seamless way. ML Jobs makes data collaboration genuinely practical, and that's what the future of advertising will be built on.” —Kalyan Lanka, Chief Product Officer, Affinity Solutions

"With Snowflake's ML Jobs now available in Snowflake's Data Clean Room Collaboration API, Kroger Precision Marketing is able to connect our industry-leading media measurement sciences natively within Snowflake Data Clean Rooms. This innovation unlocks our best-in-class incrementality sciences, allowing KPM to deliver trusted, privacy-first insights at the speed and scale modern marketers demand." —Nathan Dall, Product, Data Collaboration & AI, Kroger Precision Marketing

What this enables

Some of the most valuable ML models in advertising, financial services and healthcare require data from multiple organizations that have generally not been able to share it because moving it across company boundaries creates privacy, or competitive problems. Clean room ML is designed to address that directly.

Multiparty model training and scoring

A machine learning model trained on a single data silo reflects only that silo's view of the world. A retail media network, a financial services provider and a brand each have distinct signals about consumer behavior such as purchase history, transaction patterns and engagement data that are far more predictive in combination than in isolation.

ML Jobs makes it practical to run a single training pipeline across feature sets from multiple parties. Each organization contributes to the model without exposing raw records to the others. The accuracy improvement from richer, multi-source features is one of the clearest arguments for collaborative ML.

Propensity scoring is a direct example of this. A publisher scores their full user population for purchase propensity on behalf of an advertiser, but a propensity model trained only on the publisher's behavioral signals is limited. Bring in the advertiser's first-party conversion history and a data provider's demographic and psychographic signals, and the same model trains on a far richer feature set: content affinity and session behavior from the publisher, income tier and life stage from the data provider, and actual purchase outcomes from the advertiser. The resulting scores are meaningfully more predictive because the model has seen the full picture of what drives conversion. Models that previously relied on a single publisher's behavioral signals can now be retrained with each new campaign using the full set of available features, keeping scores fresh rather than degrading as audience composition shifts.

Ease of development, deployment and scale

For ML engineers and data scientists, the experience inside a clean room collaboration is the same as outside it: Write standard Python, use any packages you need and work in whatever IDE or notebook environment you prefer. The only addition is a short YAML code spec that declares what to run and what it needs:

ml_jobs:

• name: train_model

entrypoint: train.py

stage_code_dir: '@my_db.public.ml_stage/project'

pip_requirements:

• xgboost

• scikit-learn

• pandas

No Docker images to build, no container...