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Tensor Networks have emerged as a prominent alternative to neural networks for addressing machine learning challenges in foundational sciences, offering a bridge between quantum information techniques and classical learning algorithms. This transfer of methods from quantum information to machine learning has the potential to enhance model interpretability and efficiency, paving the way for applications to real-life problems. This work introduces tn4ml, a library designed to seamlessly integrate Tensor Networks into optimization pipelines for machine learning tasks. Inspired by existing machine learning frameworks, the library offers a user-friendly structure with modules for data embedding, objective function definition, model training using diverse optimization strategies and evaluation. We demonstrate its versatility through examples such as supervised learning on tabular data and unsupervised learning on the MNIST dataset. Additionally, we analyze how customizing the parts of the machine learning pipeline for Tensor Networks influences different performance metrics for specific tasks. Beyond these examples, tn4ml has been used in high-energy physics applications, including anomaly detection in the latent space of LHC collision events, and quantized Tensor Network models for low-latency jet tagging.