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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.
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.
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.
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