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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
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
Watch: Effective Transfer Learning for NLP
Transfer learning, the practice of applying knowledge gained on one machine learning task to aid the solution of a second task, has seen historic success in the field of computer vision. The output representations of generic image classification models trained on ImageNet have been leveraged to build models that... Read more
Watch: The Future of Machine Learning
See the video from Accelerate AI West 2019 where keynote, Alex Holub, talks about where the biggest innovations in applied Machine Learning will occur in the next 5 years. He is discussing how some of the largest global organizations are using Machine Learning today, and the near future of... Read more
ML Operationalization: From What and Why? to How and Who?
Operationalization may be the newest 18 letter word in AI, but there are specific steps to removing your AI initiative from the silos and putting it into production at scale. Sivan Metzger of ParallelM is here to share his experiences, mistakes and all, deploying machine learning and building a... Read more