End-to-end model development, deployment, & monitoring

A common platform for data scientists and IT

Build models in Domino with the scalability and power of Snowflake for in-database computation

Accelerated Model Development

In a few clicks, data science teams have self-service data science workspaces with governed, secure access to data in Snowflake, pre-configured with curated tools, packages, frameworks, and compute for model development and training at scale - no DevOps required.

Flexible Model Deployment

Improve prediction response time for critical applications by deploying models and executing Python scoring code inside Snowflake Data Cloud, using the scalability and power of Snowflake for in-database computation. Simplify enterprise infrastructure with a common data and deployment platform.

Real-Time Model Monitoring

Simplify model management with automated prediction data capture pipelines and monitoring for models deployed to Snowflake Data Cloud. Ensure model accuracy with continuously updated data drift and model quality calculations to make better business decisions.

Built for Data Science Teams

Provide project management, collaboration, and reproducibility across code-first data science teams while flexibly supporting the tools, packages, and compute frameworks (i.e., Spark, Ray, and Dask) of choice. Compound knowledge instead of reinventing the wheel.

End-to-end Data Science Lifecycle with Domino and Snowflake Snowpark

Data & Infrastructure without DevOps

Accelerate Model Development

Domino natively integrates with Snowflake - with credentials or OAuth. With just a few clicks, data science teams have immediate access to data residing in Snowflake - without having to orchestrate the movement of data through manual workarounds.
From Domino, Data scientists can build and train models using Snowflake Snowpark, using Python-based libraries in Snowflake’s compute environments where the data resides.

Flexible Model Deployment

Co-locate Models and Data

Combine the flexibility of model building in Domino with the scalability and power of Snowflake for in-database computation.
User-defined functions (UDFs) for ML inference built in Domino are optimized to execute asynchronously on Snowflake, so data scientists can execute Python scoring code directly in Snowflake - where the data resides - to improve security and prediction response time for mission-critical applications.
A single pane of glass shows all exported models with performance indicators, hosting settings, and metadata.

Real-Time Model Monitoring

Ensure Prediction Accuracy

Automatically configure models for drift and model quality analysis. With a few easy steps, set up prediction data capture pipelines and ground truth datasets.
Configure drift tests and thresholds on a per-feature basis, with automated user notifications - all in Domino’s simple interface. Automatically identify when new production data is available, and update data drift and model calculations.
Enforce best practices for optimal model performance and better decision-making.

Explore Domino and Snowflake Resources


Build, Deploy, and Monitor Models in Snowflake using Domino and Snowpark

Learn more

On-Demand Webinar

Snowflake + Domino: More Datasets, More Production Models, More Business Impact...Zero DevOps

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Solution brief

Learn more about Domino and Snowflake: A common data and model deployment platform

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Partnership News

Domino Press

Domino Data Lab Announces Investment from Snowflake to Unite ML Models and Cloud Data in One Platform

June 2022

Snowflake blog

Snowflake invests in Domino 
Data Lab to enable data science model training, deployment, and monitoring on Snowflake Data Cloud

June 2022


Domino Data Lab Deepens Integration with Snowflake to Help Mutual Customers Accelerate Returns on Data Science

June 2022

Become a Partner

Domino’s growing partner ecosystem helps our customers accelerate the development and delivery of models with key capabilities of infrastructure automation, seamless collaboration, and automated reproducibility. This greatly increases the productivity of data scientists and removes bottlenecks in the data science lifecycle.