Blogs and other references#

The RAPIDS team blogs at https://medium.com/rapids-ai, and many of these blog posts provide deeper dives into models or key features from cuML. Here, we’ve selected just a few that are of particular interest to cuML users:

Integrations, applications, and general concepts#

• RAPIDS Configurable Input and Output Types

• RAPIDS on AWS Sagemaker

Tree and forest models#

• Accelerating Random Forests up to 45x using cuML

• RAPIDS Forest Inference Library: Prediction at 100 million rows per second

• Sparse Forests with FIL

Other popular models#

• Accelerating TSNE with GPUs: From hours to seconds

• Combining Speed and Scale to Accelerate K-Means in RAPIDS cuML

• Accelerating k-nearest neighbors 600x using RAPIDS cuML

Academic Papers#

• Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence (Sebastian Raschka, Joshua Patterson, Corey Nolet)