# KeystoneML: Optimizing Pipelines for Large-Scale Advanced Analytics

arXiv: https://arxiv.org/abs/1610.09451 · HTML: https://mlsanity.com/paper/1610.09451/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Oct 2016.

## Built on by (most cited first, 13 of 13)

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- [Couler: Unified Machine Learning Workflow Optimization in Cloud](https://mlsanity.com/paper/2403.07608/) (2024)
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## Abstract (arXiv)

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.