Leverage your Data Warehouse for innovative machine learning (ML) projects such as churn analysis or customer lifetime value projections.
Leverage all the structured data in the data warehouse and combine it with unstructured data to provide a comprehensive view for the Data Scientist.
Pachyderm’s pipelines leverage automated versioning that drives incremental processing and data deduplication that shorten processing times and reduce storage costs
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
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
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
Learn more about this feature from our documentation website on how to leverage SQL data sources in your ML Pipelines.
Create a churn analysis model for a music streaming service with Pachyderm and Snowflake using the Data Warehouse integration.
Going beyond the limitations of SQL and using Python to speed development and insight with Snowflake.