As a Data Scientist at DataInfer, much of my work involves designing and improving machine learning models, including Neural Networks. One of the crucial parts of my job is ensuring the interpretability of these models. Without an understanding of what these networks are doing, it's impossible to confidently use them for prediction and decision-making.
One project that comes to mind is when we were building a Recurrent Neural Network (RNN) for predicting future sales. For this, we wanted to understand which past time steps the RNN was considering when making its predictions. To do this, we used an interpretation technique called Layer-wise Relevance Propagation (LRP). LRP allowed us to see which past sales were given higher importance and whether that aligned with our business understanding.
However, it was challenging due to the model's complexity and the countless hours spent tuning the model. But we were able to achieve better transparency into our model and deliver more accurate and trustworthy predictions, reiterating the importance of model interpretability.