The development process for machine learning and artificial intelligence projects is quite acyclic. Model and AI Application development can always be enhanced by more visualization and exploration. However, we also need to get the project done. What is the best balance of visual exploration and application release? There are models that are commonly used during model development that enhance our understanding of which features have the greatest impact. These models may not be the best models for predictions, but this does not mean that they cannot be used for explanation. Furthermore, there are techniques that can be used to show feature significance within the model chosen for use and production. Thom will discuss these approaches and explain their application.
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