Query spelling correction is a crucial component of moden search engines that can help users to express an information need more accurately and thus improve search quality. In participation of the Microsoft Speller Challenge, we proposed and implemented an efficient end-to-end speller correction system, namely CloudSpeller. The CloudSpeller system uses a Hidden Markov model to effectively model major types of spelling errors in a unified framework, in which we integrate a large-scale lexicon constructed using Wikipedia, an error model trained from high confidence correction pairs, and the Microsoft Web N-gram service. Our system achieves excellent performance on two search query spelling correction datasets, reaching 0.970 and 0.940 F1 scores on the TREC dataset and the MSN dataset respectively.