This paper is associated with a tutorial presented at DEXA 2025 Conferences and Workshops. The tutorial shares the practical experience gained from a 3-year R&D project for a big financial institution in Poland. The project aimed at developing deduplication pipelines for customer records. It involved the development of two distinct end-to-end deduplication pipelines that are based on (1) statistical/probabilistic modeling and on (2) machine learning. This tutorial focuses on lessons learned from developing the machine learning pipeline, within the context of a real-world industrial setting. Moreover, this tutorial provides an overview of approaches to data deduplication, including the traditional state-of-the-art baseline deduplication pipeline, solutions based on machine learning and neural networks that apply pre-trained and large language models.