Snowflake launches ML Jobs for privacy-compliant multi-party data collaboration

This release shifts data clean rooms from simple SQL-based compliance tools to scalable development environments for proprietary IP. By supporting distributed Python and GPU compute, Snowflake removes the memory constraints that previously limited multi-party collaboration to basic lookalike models. For the streaming and advertising ecosystem, this provides a standardized way to execute complex causal lift measurement and probabilistic identity resolution without data intermediaries. Watch for whether this reduces reliance on third-party identity providers as publishers and brands move toward direct, automated cross-cloud model training.

The general availability of ML Jobs follows a broader trend of cloud providers embedding advanced artificial intelligence directly into governed data environments. As third-party cookie deprecation continues to challenge traditional targeting, the industry has shifted toward these private collaboration layers. Per Decentriq (March 2026), the competitive landscape for data clean rooms has bifurcated into platform-managed models from Snowflake, AWS, and Databricks versus cloud-independent neutral environments like InfoSum and LiveRamp (Habu). Snowflake’s latest move specifically targets Databricks’ historical lead in technical ML breadth by integrating native support for Spark-like Python workloads through Snowpark.

Recent market activity highlights the growing value of these integrations. According to Marketing Dive (November 2021), retail giants like Kroger have long sought to leverage transaction-level data from millions of households to court brand dollars, a process that originally required significant custom infrastructure. In early 2026, industry reports from Fivetran and PuppyGraph indicate that enterprises are increasingly evaluating clean room providers based on 'zero-copy' capabilities—the ability to share data across regions and clouds without physical movement—to avoid the 'double taxation' of data egress fees.

Furthermore, the acquisition of Samooha by Snowflake in 2023 provided the foundational no-code UI currently layered atop these ML capabilities. This technical evolution mirrors moves by AWS Clean Rooms, which expanded its official participant limit to five parties to accommodate more complex supply chain and retail media collaborations. As of mid-2026, the convergence of advertising and AI is accelerating adoption, with Snowflake, Azure, and Databricks now competing to provide the default infrastructure for training proprietary AI agents on multi-party consumer signals.