Machine Learning in Production: Software Architecture
Special thanks to Addison-Wesley Professional for permission to excerpt the following "Software Architecture" chapter from the book, Machine Learning in Production. This chapter...
Comparing the Functionality of Open Source Natural Language Processing Libraries
In this guest post, Maziyar Panahi and David Talby provide a cheat sheet for choosing open source NLP libraries. What do natural language...
Manipulating Data with dplyr
Special thanks to Addison-Wesley Professional for permission to excerpt the following "Manipulating data with dplyr" chapter from the book, Programming Skills for Data...
Reflections on the Data Science Platform Market
Reflections Before we get too far into 2019, I wanted to take a brief moment to reflect on some of the changes we’ve...
Code
Creating Multi-language Pipelines with Apache Spark or Avoid Having to Rewrite spaCy into Java
In this guest post, Holden Karau, Apache Spark Committer, provides insights on how to create multi-language pipelines with Apache Spark and avoid rewriting...
Data Scientist? Programmer? Are They Mutually Exclusive?
This Domino Data Science Field Note blog post provides highlights of Hadley Wickham’s ACM Chicago talk, “You Can’t Do Data Science in a GUI”....
Machine Learning
On Ingesting Kate Crawford’s “The Trouble with Bias”
Kate Crawford discussed bias at a recent SF-based City Arts and Lectures talk and a recording of the discussion will be broadcast, May...
The Machine Learning Reproducibility Crisis
Pete Warden is the Technical Lead on the TensorFlow Mobile Embedded Team at Google doing Deep Learning. He is formerly the CTO of...
Practical Techniques
Highlights from the Maryland Data Science Conference: Deep Learning on Imagery and Text
Niels Kasch, cofounder of Miner & Kasch, an AI and Data Science consulting firm, provides insight from a deep learning session that occurred...
Learn from the Reproducibility Crisis in Science
Key highlights from Clare Gollnick’s talk, “The limits of inference: what data scientists can learn from the reproducibility crisis in science”, are covered...
Leaders at Work
Collaboration Between Data Science and Data Engineering: True or False?
This blog post includes candid insights about addressing tension points that arise when people collaborate on developing and deploying models. Domino’s Head of...
Data Science Use Cases
In this post, Don Miner covers how to identify, evaluate, prioritize, and pick which data science problems to work on next. Don is...
Model Interpretability with TCAV (Testing with Concept Activation Vectors)
This Domino Data Science Field Note provides very distilled insights and excerpts from Been Kim’s recent MLConf 2018 talk and research about Testing with...
Trust in LIME: Yes, No, Maybe So?
In this Domino Data Science Field Note, we briefly discuss an algorithm and framework for generating explanations, LIME (Local Interpretable Model-Agnostic Explanations), that...
Classify all the Things (with Multiple Labels)
Derrick Higgins of American Family Insurance presented a talk, “Classify all the Things (with multiple labels): The most common type of modeling task no...
Put Models at the Core of Business Processes
At Rev, Nick Elprin, Domino's CEO, continued to provide insights on managing data science based upon years of candid discussions with customers. He...