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RAPIDS Forest Inference Library: Prediction at 100 Million Rows per Second
Random forests (RF) and gradient-boosted decision trees (GBDTs) have become workhorse models of applied machine learning. XGBoost and LightGBM, popular packages implementing GBDT models, consistently rank among the most commonly used tools by data scientists on the Kaggle platform. We see similar interest in forest-based models in industry, where... Read more
Automating Image Annotation with MAX
This blogpost introduces a use-case example to automate image annotation with MAX (Model Asset Exchange). To learn more about how our deep learning models are created, containerized, and deployed to production, come join our training at ODSC West 2019: Deploying Deep Learning Models as Microservices.  Introduction The Model Asset Exchange... Read more
Unpacking YouTube’s Recommender System
Over the past couple of years, YouTube has come under fire for its recommender system, with the media suggesting that it is promoting violent content, or banning LGBT content for violating its terms of service. Seemingly in response to all of this, Google has finally released a paper explaining... Read more
Composable Machine Learning
Even as machine learning (ML) algorithms become more sophisticated and powerful, the way ML teams build ML systems hasn’t changed much. In this article, we’ll explain the need for composable machine learning systems. First, take a look at the old, inefficient way. Once the team figures out the task... Read more
Applications of AI in Cybersecurity
Editor’s Note: See Dustin’s talk “Applications of AI in Cybersecurity” at ODSC West 2019. Security has historically lagged behind the implementation of new technology. With AI/ML transforming how industries and government agencies do business and serve citizens, it is critical that developers build security into our architectures from the... Read more
Using GANs to Generate Images of Race Cars
Have you ever stood in a packed crowd, seen heatwaves rippling off the pavement and felt the roar of cars flying past you? No? I’m guessing that the overlap of ODSC blog readers and car racing fans might be small. However, those roaring cars do present some interesting data... Read more
Visualizing Vectors: Basics Every Data Scientist Should Know
This ODSC West 2018 talk “Visualizing Vectors: Basics Every Data Scientist Should Know,” presented by Jed Crosby, Head of Data Science at Clari, should be a required learning resource for all new data scientists. This is because every data scientist should have a firm grasp of the mathematics behind... Read more
RAPIDS 0.9: A Model Built To Scale
If you know me, you know I love to reference songs, sports, movies, and TV shows as analogies. The RAPIDS 0.9 release brought to mind another classic “9” that is remembered fondly by many of our team members, Star Trek: Deep Space Nine (DS9). The show has many lines that are great analogies... Read more
AI Black Box Horror Stories – When Transparency was Needed More than Ever
Arguably, one of the biggest debates happening in data science in 2019 is the need for AI explainability. The ability to interpret machine learning models is turning out to be a defining factor for the acceptance of statistical models for driving business decisions. Enterprise stakeholders are demanding transparency in... Read more
cuSpatial Accelerates Geospatial and Spatiotemporal Processing
The Internet of Things (IOT) has spawned explosive growth in sensor data. Location is some of the most important information generated by sensors, and dynamic location is vital in the case of mobile sensors. Examples include: mobile phones (GPS), vehicles, robots, and cameras. [Related Article: The Best Machine Learning... Read more