MMAP, published by Bioinformatics, is a cloud computing platform for Mining the Maximum Accuracy of Prediction (MMAP) in genomics. The platform allows users to simply upload input data (phenotypes and genotypes), click the submission button, and download the prediction results that are the optimum among the eight implemented prediction methods for the specific input data. The eight methods include gBLUP, compressed BLUP, Ridge Regression, Bayes A, Bayes B, Bayes C, Bayes Cpi, and Bayesian LASSO. MMAP is designed to solve three challenges in genomic selection. First, the best prediction method varies based on the genetic architectures of a trait and input data, including the number of genes that control the trait and its heritability in the input data. MMAP automatically identify the optimum method and output the prediction results accordingly. The identification is based on the similarity of the input data and the previous data termed “knowledge” and the “tries” guided by method structure and new prediction results on the input data. The user also has the option to choose a specific prediction method; Second, MMAP eliminates the frustration for install and maintain these different prediction software packages; Third, MMAP is consists of multiple servers with large RAM and a number of nodes to overcome the hardware threshold that users may have. As MMAP is knowledge-based optimization, the more usage, the more efficient the MMAP identifies the best prediction.