There has been an exponential increase in the volume and variety of data at our disposal to build recommenders and notable advances in compute and algorithms to utilize in the process. Particularly, the means to store, process and learn from image data has dramatically increased in the past several years. This allows retailers to go beyond simple collaborative filtering algorithms and utilize more complex methods, such as image classification and deep convolutional neural networks, that can take into account the visual similarity of items as an input for making recommendations. This is especially important given online shopping is a largely visual experience and many consumer goods are judged on aesthetics.
In this article, we’ll change the script and show the end-to-end process for training and deploying an image-based similarity model that can serve as the foundation for a recommender system. Furthermore, we’ll show how the underlying distributed compute available in Databricks can help scale the training process and how foundational components of the Lakehouse, Delta Lake and MLflow, can make this process simple and reproducible.
Click through for the process.