June 7th, 2022

Improving Machine Learning Operations with H2O.ai and Snowflake

RSS icon RSS Category: Cloud, H2O AI Cloud, MLOps, Snowflake

Operationalizing models is critical for companies to get a return on their machine learning investments, but deployment is only one part of that operationalization process. With H2O.ai’s latest Snowflake Integration Application, authorized Snowflake users can easily deploy models, significantly reducing deployment timelines and enabling a level of self-service that creates faster time to value.

H2O.ai provides a comprehensive suite of capabilities surrounding machine learning operations that support data scientists and machine learning engineers in the deployment, management and monitoring of their models in production.

It is critical to manage and monitor deployed machine learning models to ensure models are operating as intended. Metrics detailing model execution can be collected and made available to Snowflake users.

Using Snowflake’s new Event Tables feature, models that are deployed as Java UDF’s can now have these metrics collected and reported within the Snowflake Integration Application. This added capability allows data scientists to deploy and verify the operations of their models within the Snowflake environment.

This ability enables data scientists to deploy machine learning models into the Snowflake environment for testing and scoring the data within the UI. They can also verify the results and operational metrics before promoting models to production. The production team can use the same UI to deploy the model into the Snowflake environment.

The integration uses the Snowflake Java User Defined Function to enable execution of models within the Snowflake environment. This moves machine learning models to where the data lives, in the Snowflake Data Cloud, to provide faster scoring latency performance.  The UDF scales with the warehouse as the data size grows.

The new Event Table support allows key runtime metrics to be written back to Snowflake.  Those events can be then viewed using the UI to achieve operational visibility with near zero impact to the execution profiles of the models.

These new features make it easy for organizations to operationalize models and combine the values delivered by the H2O AI Cloud and Snowflake’s Data Cloud.

Try the Snowflake Integration Application today with our free 90-day trial.

 



About the Author

Eric Gudgion
Eric Gudgion

Eric is a Senior Principal Solutions Architect, he is passionate about performance and scalability. Eric’s role enables him to help customers adopt h2o within their enterprises.

Leave a Reply

+
A Brief Overview of AI Governance for Responsible Machine Learning Systems

Our paper “A Brief Overview of AI Governance for Responsible Machine Learning Systems” was recently

November 30, 2022 - by Navdeep Gill, Abhishek Mathur and Marcos V. Conde
+
H2O World Dallas Customer Talks

After three long years of not having an #H2OWorld, we finally held our first one

November 24, 2022 - by Vinod Iyengar
+
New in Wave 0.24.0

Another Wave release has arrived with quite a few exciting new features. Let's quickly go

November 21, 2022 - by Martin Turoci
Fallback Featured Image
+
H2O.ai Raises $40 Million to Democratize Artificial Intelligence for the Enterprise

Series C round led by Wells Fargo and NVIDIA MOUNTAIN VIEW, CA – November 30, 2017

November 20, 2022 - by
+
H2O.ai Placed Furthest in Completeness of Vision in 2021 Gartner Data Science and Machine Learning Magic Quadrant in the Visionaries Quadrant. — Copy

At H2O.ai, our mission is to democratize AI, and we believe driving value from data

November 18, 2022 - by Read Maloney, SVP of Marketing
+
H2O.ai Expands Market Footprint in Healthcare AI by Signing Hackensack Meridian Health and Other Key Providers

We’re excited to attend the HLTH conference this week in Las Vegas, NV. This industry

November 14, 2022 - by Prashant Natarajan

Start Your Free Trial