Spotify Annoy (approximate nearest neighbor Oh Yeah!) is an open-source algorithm used by Spotify for identifying similar sounding songs for recommendations to users. Spotify Annoy can also be used to create a search index for similar images, which has many real-world implementations including recommending products in on-line stores. Creating a similar image search index can be accomplished in a few lines of Python code, but how can this process be automated, and the index published as an API that can be consumed by other applications?

In this demo intensive session Alan will run through the process of creating an image similarity search API hosted in Azure Machine learning. Starting with the creation of an image dataset he will create an Azure ML experiment to use a pre-trained PyTorch model to create an approximate nearest neighbor index using Spotify Annoy. He will then create an endpoint in Azure ML that return images that are similar to a target image. Throughout the process he will explain the theory of using PyTorch and Spotify Annoy and how the features of Azure ML Studio can be leveraged for the rapid cloud-based development of machine learning solutions.