Why models fail to deliver value and what you can do about it.
Building models requires a lot of time and effort. Data scientists can spend weeks just trying to find, capture and transform data into...
Data Drift Detection for Image Classifiers
This article covers how to detect data drift for models that ingest image data as their input in order to prevent their silent...
Model Interpretability: The Conversation Continues
This Domino Data Science Field Note covers a proposed definition of interpretability and distilled overview of the PDR framework. Insights are drawn from...
On Being Model-driven: Metrics and Monitoring
This article covers a couple of key Machine Learning (ML) vital signs to consider when tracking ML models in production to ensure model...
Code
The importance of structure, coding style, and refactoring in notebooks
Notebooks are increasingly crucial in the data scientist's toolbox. Although considered relatively new, their history traces back to systems like Mathematica and MATLAB....
Natural Language in Python using spaCy: An Introduction
This article provides a brief introduction to natural language using spaCy and related libraries in Python. The complementary Domino project is also available....
Machine Learning
Deep Learning Illustrated: Building Natural Language Processing Models
Many thanks to Addison-Wesley Professional for providing the permissions to excerpt "Natural Language Processing" from the book, Deep Learning Illustrated by Krohn, Beyleveld,...
Make Machine Learning Interpretability More Rigorous
This Domino Data Science Field Note covers a proposed definition of machine learning interpretability, why interpretability matters, and the arguments for considering a...
Practical Techniques
Techniques for Collecting, Prepping, and Plotting Data: Predicting Social Media-Influence in the NBA
This article provides insight on the mindset, approach, and tools to consider when solving a real-world ML problem. It covers questions to consider...
Time Series with R
This article delves into methods for analyzing multivariate and univariate time series data. A complementary Domino project is available. Introduction Conducting exploratory analysis...
Leaders at Work
Why models fail to deliver value and what you can do about it.
Building models requires a lot of time and effort. Data scientists can spend weeks just trying to find, capture and transform data into...
Data Science at The New York Times
Chris Wiggins, Chief Data Scientist at The New York Times, presented "Data Science at the New York Times" at Rev. Wiggins advocated that...
HyperOpt: Bayesian Hyperparameter Optimization
This article covers how to perform hyperparameter optimization using a sequential model-based optimization (SMBO) technique implemented in the HyperOpt Python package. There is...
Deep Reinforcement Learning
This article provides an excerpt "Deep Reinforcement Learning" from the book, Deep Learning Illustrated by Krohn, Beyleveld, and Bassens. The article includes an...
Towards Predictive Accuracy: Tuning Hyperparameters and Pipelines
This article provides an excerpt of “Tuning Hyperparameters and Pipelines” from the book, Machine Learning with Python for Everyone by Mark E. Fenner....
Seeking Reproducibility within Social Science: Search and Discovery
Julia Lane, NYU Professor, Economist and cofounder of the Coleridge Initiative, presented “Where’s the Data: A New Approach to Social Science Search &...