Hopsworks feature store can be configured to leverage the content of data warehouses to simplify the data science workflow. For data scientists, using data directly from a data warehouse presents three challenges:

• data in the data warehouse is often updated making it impossible to reproduce previously generated training data and previous experiments.

• Data warehouses often lack the historical view of the data, leaving to data scientists the chore of building it.

• Finally productionizing a model often requires building additional pipelines to make the same data available in a low latency database for online serving.

RESERVE YOUR SEAT

What is this talk about?

In this talk, we will discuss how Hopsworks can be connected to existing cloud-native data warehouses like Snowflake, Redshift and BigQuery. We will show how to use data warehouses as a source of data to build historical and reproducible training dataset. We will show how to leverage the core functionalities of Hopsworks: Python centric APIs, time travel, statistics, search and data validation to build historical, clean and reproducible dataset to train and productionize machine learning models.

Join us!

• What: Leverage your Snowflake, BigQuery, Redshift Data Warehouse with a Real-Time Feature Store

• Who: Fabio Buso, VP of Engineering at Hopsworks

• When: Wednesday, September 21st | 9 AM PDT

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