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.
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.
In this webinar, Fabio Buso, VP of Engineering at Hopsworks will present the new 3.0 release; How our new Feature View and write APIs have evolved to help Data Scientists bring their models to production, and other state-of-the art new improvements.
In this webinar we will explain the core concepts of great expectations and how we made them available on Hopsworks to be used within your feature pipelines. Users are able to define expectation suites or reuse their existing ones.
The modern data team consists of Data Engineers, Data Scientists, Machine Learning Engineers, and Business Analysts, but effective collaboration is challenging. In this talk we will show how a centralized data platform helps solve these issues.
The Feature Store is the essential part of AI infrastructure that helps organisations bring modern enterprise data to analytical and operational ML systems. It is the simplest most powerful way to get your models to production. From anywhere, to anywhere.From months, to minutes.