Easily Build Models on top
of your Data Warehouse
Flexible, version-controlled machine learning with data warehouse
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 pipelines leverage automated versioning that drives incremental processing and data deduplication that shorten processing times and reduce storage costs
Complex Data Workflows
With Pachyderm you can build complex workflows that can support the most advanced ML applications, which can be visually managed and monitored with Pachyderm console UI
Scales to the Job
Pachyderm scales to petabytes of data with autoscaling and data-driven parallel processing. Our approach to version control and file processing automates scale while controlling compute costs
Pachyderm automatically versions all data and code changes across your data workflow, including intermediate transformations, so you always have full reproducibility and lineage for your ML models
Language and data agnostic
Use any language or library in your Pachyderm pipelines such as Python, R, Scala, or Bash. If you can get it into a container, then Pachyderm can run it as a pipeline. Easily process both structured and unstructured data
Getting data into and out-of your data warehouse is as simple as writing a SQL query.
Pachyderm & Snowflake
Read our blog about speeding up pipeline development with Pachyderm and Snowflake
Churn Prediction Example
View the churn prediction example on GitHub
Read the Docs
Learn more about this feature from our documentation page
See Pachyderm In Action
Watch a short 5-minute demo which outlines the product in action
Want to see Pachyderm Data Pipelines in action? Book a demo with one of our solution engineers!