<div>Machine Learning (ML) techniques now are ubiquitous tools to extract useful information from data collections. With the increasing volume of data, large-scale ML applications require an efficient implementation to accelerate the performance. In this talk, I will introduce a new system, named Angel, to facilitate the development of large-scale ML applications in production environment. Angel employed hybrid parallelism to accelerate the performance of ML algorithms. The pulling of parameters and the pushing of updates were fully optimized in Angel to reduce the network overheads. Angel has been deployed in a Tencent production cluster with thousands of nodes and supports various applications. </div>