Data

AI and ML in the data cloud: the next frontier

Joey Lee

January 2, 2026

Abstract illustration of a hand managing data on a mobile device, with heart and lung icons representing healthcare marketing automation.

As artificial intelligence becomes central to how organizations operate, the role of the data platform is changing. Analytics alone is no longer enough. Teams want to train models, generate predictions, and apply AI directly to the data they already trust.

Snowflake’s strategy reflects this shift. By expanding beyond analytics into machine learning and generative AI, Snowflake is positioning the data cloud as a foundation for data intelligence. Rather than replacing specialized AI platforms, Snowflake aims to bring AI closer to where data already lives.

Why AI is moving into the data platform

Historically, machine learning workflows required moving data out of the warehouse into separate systems. This created friction around data movement, security, and governance.

Modern organizations want to reduce that complexity. By enabling AI and ML directly within the data platform, teams can:

Minimize data movement
Apply consistent governance and access controls
Shorten the path from insight to action

Snowflake’s architecture makes this approach possible at scale.

Snowpark ML: machine learning inside Snowflake

Snowpark ML allows data scientists and engineers to build, train, and deploy machine learning models directly within Snowflake. It supports Python-based workflows and integrates with popular libraries used in data science.

With Snowpark ML, teams can:

Perform feature engineering using warehouse-scale data
Train models without exporting data
Deploy models as part of production pipelines

This approach keeps data centralized and reduces operational overhead.

Cortex: generative AI in the data cloud

Cortex is Snowflake’s managed AI service that brings large language model capabilities directly to data stored in Snowflake. It allows teams to apply AI functions such as summarization, classification, and sentiment analysis using SQL or Python.

Cortex is designed to work with enterprise data while maintaining strong security and governance. Sensitive data stays inside Snowflake, and access is controlled using existing role-based permissions.

By embedding generative AI into the data platform, Snowflake lowers the barrier for teams to experiment with AI-driven insights.

Working with external AI and LLM platforms

Snowflake does not aim to replace external AI platforms. Instead, it integrates with them.

Organizations can use Snowflake as the data foundation while training and serving models using tools like Amazon SageMaker, Google Vertex AI, or Azure Machine Learning. Data can flow securely between Snowflake and these platforms using native integrations and secure data sharing.

This hybrid approach allows teams to choose the best tools for their needs while maintaining a single source of truth.

AI-powered analytics and data intelligence

Beyond model training, AI is increasingly used to enhance analytics itself. Snowflake is incorporating AI-driven features into query optimization, data discovery, and natural language access.

These capabilities help users find insights faster and reduce the technical barrier to working with data. As AI matures, the data cloud becomes not just a storage layer but an intelligent system that actively assists users.

Why this matters for innovation teams

For innovation and data science teams, Snowflake’s AI strategy offers a balance between flexibility and control. Teams can experiment quickly without building complex infrastructure, while still meeting enterprise requirements around security and governance.

This makes Snowflake attractive for organizations that want to operationalize AI rather than keep it confined to research projects.

The future of the data cloud

The next phase of the data cloud is about intelligence, not just scale. Data platforms will increasingly support AI-driven workflows as a core capability.

Snowflake’s investments in Snowpark ML, Cortex, and AI integrations signal a clear direction. The data cloud is becoming the place where analytics, machine learning, and generative AI converge.

For organizations thinking about the future of data, this convergence matters. The closer AI is to trusted data, the faster teams can turn ideas into impact.

In the next post in this series, we will examine Snowflake’s business model and ecosystem strategy and why it has proven so effective at driving adoption and growth.