Model Interpretation: What and How?
Editor’s note: Brian is a speaker for ODSC West in California this November! Be sure to check out his talk, “Advanced Methods for Explaining XGBoost Models” there! As modern machine learning methods become more ubiquitous, increasing attention is being paid to understanding how these models work — model interpretation instead... Read more
Redefining Robotics: Next Generation Warehouses
People picture robots changing to look more like humans, but in reality, the evolution of robotics involves things you can’t actually see. For Bastiane Huang at Osaro, the development of robots means greater advances in autonomy. Building brains for robots gives them more flexibility for tasks and creates more... Read more
Recognize Class Imbalance with Baselines and Better Metrics
Editor’s Note: Samuel is speaking at ODSC West 2019, see his talk “Help! My Classes are Imbalanced” there. In my first machine learning course as an undergrad, I built a recommender system. Using a dataset from a social music website, I created a model to predict whether a given... Read more
Not Always a Black Box: Machine Learning Approaches For Model Explainability
Editor’s Note: Violeta is speaking at ODSC Europe 2019, see her talk “Not Always a Black Box: Explainability Applications for a Real Estate Problem“ What is model explainability? Imagine that you have built a very precise machine learning model by using clever tricks and non-standard features. You are beyond... Read more
Watch: No Black Boxes: Understandability, Transparency, and Governance in Machine Learning
In this talk, presented at Accelerate AI East 2019, Ingo Mierswa presents the ideas of understandability, transparency, and governance in machine learning, and how those pieces all work together. Ingo Mierswa is an industry-veteran data scientist... Read more
NVIDIA GPUs and Apache Spark, One Step Closer
While RAPIDS started with a Python API focus, there are many who want to enjoy the same NVIDIA GPU acceleration in Apache Spark; in fact, we have many at NVIDIA. When RAPIDS first launched, we had a plan to accelerate Apache Spark as well as Dask, and we want to share some major accomplishments we’ve... Read more
Bias Variance Decompositions using XGBoost
This blog dives into a theoretical machine learning concept called the bias-variance decomposition. This decomposition is a method which examines the expected generalization error for a given learning algorithm and a given data source. This helps us understand questions like: – How can I achieve higher accuracy with my... Read more
10 Minutes to cuDF and Dask cuDF
Centered around Apache Arrow DataFrames on the GPU, RAPIDS is designed to enable end-to-end data science and analytics on GPUs. Together, open source libraries like RAPIDS cuDF and Dask let users process tabular data on GPUs at scale with a familiar, pandas-like API. With Dask, anything you can do... Read more
Taking Your Machine Learning from 0 to 10
Madhura Dudhgaonkar is the senior director of Machine Learning at Workday Inc. She believes that it’s possible to deploy machine learning within your enterprise, but it takes a few steps to get exactly right. She loves to get into unknowns and things we haven’t tried yet, but let’s look... Read more
Causal Inference: An Indispensable Set of Techniques for Your Data Science Toolkit
Editor’s Note: Want to learn more about key causal inference techniques, including those at the intersection of machine learning and causal inference? Attend ODSC West 2019 and join Vinod’s talk, “An Introduction to Causal Inference in Data Science.” Data scientists often get asked questions of the form “Does X... Read more