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.
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.
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.
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.
Evidation transforms research into production-grade models in as little as eight weeks with Enterprise MLOps.
"Domino and Snowflake are critical to the future of our business. They allow new use cases to keep flowing, so we can empower individuals to manage their health better before they become patients."
Biz Phillips
Data Science Manager
Data & Infrastructure without DevOps
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
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
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.
On-Demand Webinar
Solution brief
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.
