Approximate nearest neighbor search (ANNS) is a fundamental task in data science with widespread applications. In this talk, we present several techniques for scaling ANNS to large datasets. First, we introduce a new algorithm for building ANNS indices that achieves an order of magnitude speedup over earlier approaches. Furthermore, we present a general technique for decomposing a large ANNS problem into a collection of smaller problems, enabling ANNS on datasets that exceed single-machine capacity.