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.
KeystoneML REF introduces an approach for large-scale pipeline optimization.
KeystoneML: Optimizing Pipelines for Large-Scale Advanced Analytics
2017 IEEE 33rd International Conference on Data Engineering (ICDE)
2017 IEEE 33rd International Conference on Data Engineering (ICDE)
Mathematics Computer Science