  Modern advanced analytics applications make use of machine learning
techniques and contain multiple steps of domain-specific and general-purpose
processing with high resource requirements. We present KeystoneML, a system
that captures and optimizes the end-to-end large-scale machine learning
applications for high-throughput training in a distributed environment with a
high-level API. This approach offers increased ease of use and higher
performance over existing systems for large scale learning. We demonstrate the
effectiveness of KeystoneML in achieving high quality statistical accuracy and
scalable training using real world datasets in several domains. By optimizing
execution KeystoneML achieves up to 15x training throughput over unoptimized
execution on a real image classification application.
