Due to the volume, complex and fast-changing characteristics of big data, traditional machine learning platforms are not applicable. Therefore, designing an efficient and general machine learning platform for big data has become an important research issue. By introducing and analyzing the characteristics of machine learning algorithms and the data and model parallelization for large-scale machine learning, some common parallel computing models were presented. Bulk Synchronous Parallel (BSP), Stale Synchronous Parallel (SSP) computing models and the differences between BSP, SSP, and Asynchronous Parallel model (AP) were introduced. Then the typical machine learning platforms based on these parallel models and the advantages and disadvantages of these platforms were mainly introduced, and what kind of big data each typical machine learning platform was best suited for was pointed out. Finally, the typical machine learning platforms were summarized from the aspects of abstract data structure, parallel computing model and fault tolerance mechanism. Some suggestions and prospects were put forward.