Researchers have made significant strides in converting machine learning pipelines into neural networks, leveraging techniques like transfer learning and knowledge distillation. A specific approach, known as student-teacher learning, has proven effective in creating compact student neural networks that replicate the performance of larger, more complex teacher networks. This method involves training a smaller neural network to mimic the behavior of a pre-trained, larger network, resulting in efficient models that retain significant accuracy. By extending this approach, scientists can potentially convert entire machine learning pipelines into neural networks, streamlining processes and enhancing performance. The implications of this research are substantial, as it could lead to more efficient and adaptable AI systems, which in turn could impact various sectors, including security and policy, so it matters to practitioners who need to stay ahead of the curve in terms of AI advancements and their far-reaching consequences1.