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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 degradation in production....

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 Bin Yu, W....

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 as well as...

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 reliability, consistency and...

Clustering in R

This article covers clustering including K-means and hierarchical clustering. A complementary Domino project is available. Introduction Clustering is a machine learning technique that enables researchers and...

Understanding Causal Inference

This article covers causal relationships and includes a chapter excerpt from the book Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications by...

Exploring US Real Estate Values with Python

This post covers data exploration using machine learning and interactive plotting. If interested in running the examples, there is a complementary Domino project available. Introduction Models...

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. Introduction This article...

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 a complementary Domino...

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 overview of reinforcement...

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. The excerpt and...

Manual Feature Engineering

Many thanks to AWP Pearson for the permission to excerpt "Manual Feature Engineering: Manipulating Data for Fun and Profit" from the book, Machine Learning with Python...

Data Science, Past & Future

Paco Nathan presented, "Data Science, Past & Future", at Rev. This blog post provides a concise session summary, a video, and a written transcript. Session Summary...

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