Title: DeepPipe: Deep, Modular and Extendable Representations of Machine Learning Pipelines. Abstract: Finding accurate Machine Learning pipelines is essential in achieving state-of-the-art AI predictive performance. Unfortunately, most existing Pipeline Optimization techniques rely on flavors of Bayesian Optimization that do not explore the deep interaction between pipeline stages/components (e.g. between hyperparameters of the deployed preprocessing algorithm and the hyperparameters of a classifier). In this paper, we are the first to capture the deep interaction between components of a Machine Learning pipeline. We propose embedding pipelines in a deep latent representation through a novel per-component encoder mechanism. Such pipeline embeddings are used with deep kernel Gaussian Process surrogates inside a Bayesian Optimization setup. Through extensive experiments on large-scale meta-datasets, we demonstrate that learning pipeline embeddings with Deep Neural Networks significantly advances the state-of-the-art in Pipeline Optimization.