Cracking the Box: Interpreting Black Box Machine Learning Models
Intro To kick off this article, I’d like to explain the interpretability of a machine learning (ML) model. According to Merriam-Webster, interpretability describes the process of making something plain or understandable. In the context of ML, interpretability provides us with an understandable explanation of how a model behaves. Basically,... Read more
Smart Image Analysis for Omnichannel Retail Applications
Editor’s note: Abon is a speaker for ODSC West this Fall! Consider attending his talk, “Computer Vision for E-Commerce: Intelligent Analysis and Selection of Product Images at Scale” then. In retail, the role of product images is critical in delivering satisfactory customer experience. Images help online shoppers gain confidence... Read more
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
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
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
ODSC Meetup: Automated and Interpretable Machine Learning
Last week, ODSC hosted a talk by Dr. Francesca Lazzeri, Senior Machine Learning Scientist at Microsoft, on the capabilities of automated and interpretable machine learning software in Microsoft’s Azure. Notably, this talk is part of a series that covers a variety of data science topics. The talks are great... Read more