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Making Fairness an Intrinsic Part of Machine Learning
Editor’s Note: At ODSC Europe 2019, Sray Agarwal will conduct a workshop on fairness and accountability demonstrating how to detect bias and remove bias from ML models.   The suitability of Machine Learning models is traditionally measured on its accuracy. A highly accurate model based on metrics like RMSE,... Read more
Optuna: An Automatic Hyperparameter Optimization Framework
Note: Please go here to see a high-resolution version of the title image) Preferred Networks has released a beta version of an open-source, automatic hyperparameter optimization framework called Optuna. In this blog, we will introduce the motivation behind the development of Optuna as well as its features. [Related Article:... Read more
What is MLPerf?
AI might be a buzzword, but the hype is outpacing tools to ensure benchmarks. Up to this point, assessing the performance of ML software was difficult. You couldn’t just measure it objectively against other types of frameworks. Now, a collection of tech companies have released MLPerf, a consistent way... Read more
RAPIDS 0.8: Same Community New Freedoms
RAPIDS released 0.8 a few weeks back. And afterwards, like most Americans, we took off for the 4th of July holiday. Over that break, I reflected on the purpose of RAPIDS. Speed is great, building a strong community is awesome, but the true power of RAPIDS is in the enablement... Read more
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
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
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
When Less is More: A Brief Story About Feature Engineering with XGBoost
I played a minor role launching RAPIDS on Google Dataproc by refining a model that predicts taxi fare in New York City. Geographic location of passenger pick-ups and drops-offs were columns in the data. These are recorded as longitude and latitude measurements, with precision to many decimal places. One of the... Read more