Challenges for Production AI

Companies have invested in data management and AI platforms to create value from unique data and domain-specific insights. However, separate systems for data management, data preparation, and data science can make production operations difficult for machine learning projects.

Actions such as data wrangling, training models, scoring records happen outside the data platform leaving data engineers using multiple tools, writing brittle integration code, and without consistent logging across systems for governance.

Integration Overview

An operations manager or data engineer in charge of machine learning models in production using Snowflake can now manage the end-to-end ml pipeline using SQL in Snowflake. Snowflake users can conduct scoring jobs, refit models on newer data, and rebuild models.

Snowflake data engineers can prepare and clean the data for machine learning using SQL, and then a data scientist can also build new models using H2O Driverless AI based on Snowflake data. These capabilities are enabled using external function calls from Snowflake to H2O Driverless AI and the H2O Driverless AI scoring pipeline(MOJO).

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Streamlining Production Processes with H2O.ai and Snowflake

With the external function integration between Snowflake and H2O Driverless AI, data ops and IT ops can be more productive as they work on AI projects in Snowflake. They can use the SQL commands they know, which speeds their work and reduces errors.

Removing the need to go to other systems to retrain models, saves the operations team valuable time. Streamlining model maintenance also keeps high performing models running in production, so downstream users can make better decisions and improve customer experiences with AI-driven applications.