The common ML libraries that have been implemented within the RxDataScience application ecosystem include industry-standard open-source solutions that provide high-efficiency at a reduced cost.
In Python, scikit-learn is used extensively for both supervised and unsupervised models. These include algorithms ranging from basic ordinary least squares (OLS) to more advanced Bayesian ML and Neural Networks. Tensorflow and Keras is used mainly for Deep Learning use cases that require more compute-intense workloads. pyTorch on GPU provides a high degree of efficiency for numeric computing heavy workloads on a case-by-case basis. Extensive examples of Jupyter notebooks highlighting the complete workflows of various ML tasks are available on demand.
We also leverage R-based packages as and when needed for statistical and forecasting applications that can leverage the wide range of packages readily available in CRAN for both general-purpose to more specialized areas as evident in R Task-View pages. RStudio and RShiny can by used for fast and localized deployments that do not require large-scale enterprise implementation.
In order to extract optimal performance from existing and new applications, the core ML team at RxDataScience incorporates latest developments from the research community, and low-level solutions in C/C++ can be integrated into solutions.