Linear Algebra and Machine Learning are essential components of many libraries in the realm of data analytics. Moreover, it is expected that data analytics will increase in importance as a target for tool and library development in the future. To this end, we have developed several related applications on top of PlinyCompute to demonstrate the productivity and performance, including:

*A Linear Algebra library that provides a matlab-like DSL, which can be used to develop distributed Linear Algebra applications.*

*A Machine Learning library includingÂ three widely used iterative machine learning algorithms:*

**Latent Dirichlet Allocation (LDA)**, used for textual topic mining**Gaussian mixture model (GMM)**learning, used to cluster data using a mixture of high-dimensional Normal distributions**K-means clustering**