The lines between data science (DS), machine learning (ML), deep learning (DL), and data mining continue to be blurred and removed. This is great as it ushers in vast amounts of capabilities, but it brings increased complexity and a vast number of tools/techniques. It's not uncommon for DL engineers to use one set of tools for data extraction/cleaning and then pivot to another library for training their models. After training and inference, it's common to then move data yet again by another set of tools for post-processing. The RAPIDS suite of open source libraries not only provides a method to execute and accelerate these tasks using GPUs with familiar APIs, but it also provides interoperability with the broader open source community and DL tools while removing unnecessary serializations that slow down workflows. GPUs provide massive parallelization that DL has leveraged for some time, and RAPIDS provides the missing pieces that extend this computing power to more traditional yet important DS and ML tasks (e.g., ETL, modeling). Complete pipelines can be built that encompass everything, including ETL, feature engineering, ML/DL modeling, inference, and visualization, all while removing typical serialization costs and affording seamless interoperability between libraries. All experiments using RAPIDS can effortlessly be scheduled, logged and reviewed using existing public cloud options. Join our engineers and data scientists as they walk through a collection of DS and ML/DL engineering problems that show how RAPIDS running on Azure ML can be used for end-to-end, entirely GPU pipelines. This tutorial includes specifics on how to use RAPIDS for feature engineering, interoperability with common ML/DL packages, and creating GPU native visualizations using cuxfilter. The use cases presented here give attendees a hands-on approach to using RAPIDS components as part of a larger workflow, seamlessly integrating with other libraries (e.g., TensorFlow) and visualization packages.

Cybersecurity poses considerable challenges for Security Operations and IT departments at enterprises across the world. The volume of data is enormous, datasets are often heterogeneous, and rapid responses to anomalous behavior are required. As a result, data science is becoming more important to cybersecurity teams. However, integrating data science tooling with existing security information and event management (SIEM) systems is difficult both at a basic functional level and at the scale demanded by increasing volumes of cyber data. In this paper, we present CLX (Cyber Log Accelerators), a solution built on RAPIDS, the suite of open source libraries for GPU- accelerated data science. CLX provides Input/Output modules to simplify loading SIEM data into analytics workflows and IP address preprocessing functions that are on average 295x faster than their CPU equivalents. We also demonstrate that RAPIDS PageRank and Louvain are on average 2190x and 1240x faster than cost-similar CPU equivalents on data from NVIDIA's internal log processing systems. Using these accelerated tools allows cyber analysts to investigate and respond to potential threats in near real-time.

The semantic Web is the next step in the Internet's evolution. The existing Web contains a considerable amount of data; most is weakly structured. Broadly accessing the data is difficult. Previously unseen data cannot be easily autonomously understood with out a consistent semantic framework. Viewing and organizing data using shared ontologies is essential for both sharing data and for Web site interoperability. Full autonomous or semi-autonomous discovery and development of ontologies is the only feasible way to transition the existing Web to the semantic Web. One strategy is to merge existing, vetted ontologies. The pre-merger ontologies would likely be similar in some respects and different in others. When comparing ontologies, a necessarily imprecise, approximate similarity metric will be necessary. Possibly, soft computing will provide useful tools