Easily build ML models on top of your Data Warehouse!

Pachyderm Data-Driven Pipelines provide native integration to Snowflake and Redshift

Data Engineering and Science teams are increasingly looking to leverage their Data Warehouse for innovative machine learning (ML) projects such as churn analysis or customer lifetime value projections. However, getting the requisite data out of Snowflake or Redshift, and into data pipelines for experimentation and model training can be challenging.

Pachyderm’s Data-Driven Pipelines make it easy to create flexible ML workflows on top of Snowflake and Redshift. With seamless integration, automation and petabyte scalability Pachyderm will help your teams iterate faster on datasets, and get ML models to production more quickly, while providing full reproducibility.

Pachyderm’s Data Warehouse integration provides out-of-the box integration with Snowflake and Redshift. With just a SQL query you can ingest a dataset into Pachyderm and then execute long running ML jobs against it using Pachyderm’s language agnostic and scalable pipelines . All changes to your data are automatically versioned, and Pachyderm can easily egress the results back into your data warehouse.

To learn more about Pachyderm’s Data Warehouse integration see our documentation!