Democratising Big Machine Learning

云计算等技术进步已经打乱了数千家管理不稳定计算负载的企业。虽然大数据的元素现在无处不在，但仍然缺乏大规模从数据中学习的广泛解决方案-大机器学习，大数据的最终目标。最大的问题不在于缺乏分布式机器学习算法，而在于准备拟合、评估和应用统计模型所需的数据；通常是手工的、混乱的、昂贵的过程。该项目建议开发先进的数据库和统计技术，用于可扩展和高效的数据准备，目标是将大机器学习带给更广泛的用户和企业。

Technological advances such as cloud computing have disrupted thousands of businesses managing volatile compute loads. While elements of Big Data are now everywhere, still absent are wide-spread solutions for learning from data at scale-Big Machine Learning, the ultimate goal of Big Data. The greatest problems come not from a lack of distributed machine learning algorithms, but rather from preparing the data needed for fitting, evaluating and applying statistical models; often a manual, messy and costly process. This project proposes to develop advanced databases and statistical techniques for scalable and efficient data preparation, with the goal of bringing Big Machine Learning to a much broader range of users and businesses.