  In recent years, exploiting the domain-specific underlying structure of data
and its generative factors for representation learning has shown success in
various use-case agnostic applications. However, the diversity and complexity
of tabular data have made it challenging to represent these structures in a
latent space through multi-dimensional vectors. We design an autoencoder-based
framework for building general purpose embeddings, we assess the performance of
different autoencoder architectures, and show simpler models outperform complex
ones in embedding highly complex tabular data. We apply our framework to
produce plug-and-play, rich, and anonymized embeddings representing AWS
customers for usage in any model, saving up to 45% of development time, and
observe significant improvements in downstream models. Moreover, we propose a
significant improvement to the calculation of reconstruction loss for
multi-layer contractive autoencoders (CAE) by calculating the Jacobian of the
entire encoder leading to a 15% improvement in reconstruction quality when
compared to a stacked CAE.
