In this talk, we will focus on the data perspective when building machine learning pipelines. Using two examples, I will show how greenfield and brownfield data labeling differ, what you should focus on in each, and how to best leverage new technologies, frameworks, and products to build high-performing models.
The goal is to give you a better understanding of what data options you have for building machine learning pipelines (whether for classification or extraction). The ideas and concepts are based on research results from the Hasso Plattner Institute and three years of experience in consulting AI projects.
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