In today's data team made up of Data Engineers, Data Scientists, Machine Learning Engineers, and Business Analysts, organizations can improve, review, or create new competitive features and products based on insights and analysis.
However, collaborating across an organization is not always easy, as goals and requirements may collide and sometimes inhibit performance.
We will outline how the use of a feature store as the core of a machine learning pipeline is essential for scaling output and creating a positive feedback loop between insights and analytics data.
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 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.