Neural Network Conversion of Machine Learning Pipelines

📰 ArXiv cs.AI

Converting machine learning pipelines to neural networks using transfer learning and knowledge distillation

advanced Published 27 Mar 2026

Action Steps

Identify a complex machine learning pipeline to convert
Apply student-teacher learning approach to transfer knowledge from the pipeline to a smaller neural network
Use transfer learning and knowledge distillation to mimic the performance of the original pipeline
Fine-tune the neural network to optimize its performance

Who Needs to Know This

Data scientists and AI engineers can benefit from this approach to simplify and optimize their machine learning pipelines, while also improving performance and reducing complexity

Key Insight

💡 Neural networks can be used to mimic the performance of complex machine learning pipelines, simplifying and optimizing them

Key Takeaways

Converting machine learning pipelines to neural networks using transfer learning and knowledge distillation

Full Article

Title: Neural Network Conversion of Machine Learning Pipelines

Abstract:
arXiv:2603.25699v1 Announce Type: cross Abstract: Transfer learning and knowledge distillation has recently gained a lot of attention in the deep learning community. One transfer approach, the student-teacher learning, has been shown to successfully create ``small'' student neural networks that mimic the performance of a much bigger and more complex ``teacher'' networks. In this paper, we investigate an extension to this approach and transfer from a non-neural-based machine learning pipeline as

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