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Interpretability and the Rise of Shapley Values
Interpretability is a hot topic in data science this year.  Earlier this spring, I presented at ODSC East on the need for data scientists to use best practices like permutation-based importance, partial dependence, and explanations.  When I first put together this talk, a lot of it was fairly new... Read more
Community-Specific AI: Building Solutions for Any Audience
With half of the world population online, and spending over 5 hours a day there, online communities are flourishing. It is now easier than ever for niche communities to form: gamers can find other players and form teams, dating adults can find better matches, students of particular subjects can... 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 Are a Few AI Research Labs on the West Coast?
Artificial Intelligence is still a nascent technology; much of the groundbreaking work moving the industry forward is done inside AI research labs. It’s often from those labs that open source projects are started.  Institutes like Open AI, NASA’s JPL, Google Deepmind, MIT CSAIL, BAIR, The Turing Institute, and Max... 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
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
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
Bears Need to Learn as well – Practical Reinforcement Learning with TensorFlow 2.0 & TF-Agents
Editor’s Note: Oliver is speaking at ODSC West 2019, see his talk “Reinforcement Learning with TF Agents & TensorFlow 2.0: Hands On” there. Have a look at our friend Orso the bear.  Orso lives in his cave and knows his area and where he can typically find some honey.... Read more
Are Successful Data Scientists Hired or Trained?
Editor’s note: Jennifer is a speaker for ODSC West 2019 this November in San Francisco! Be sure to check out her talk, “Successful Enterprise Analytics Starts with Literacy” then! The data science valley of despair is real. Time after time, leaders who’re well-versed in case studies and industry research... Read more
Automating Data Wrangling – The Next Machine Learning Frontier
Editor’s note: Be sure to check out Alex’s talk at ODSC West 2019 this November, “The Last Frontier of Machine Learning – Data Wrangling.” Up to 95% of a data scientist’s time is spent data wrangling. Conversely, about 99% of data-scientists hate data wrangling. That’s problematic. Data wrangling tends... Read more