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Can Data Science Help Us Make Sense of the Mueller Report?

This blog post provides insights on how to apply Natural Language Processing (NLP) techniques. A complementary Domino project is available. The Mueller Report The Mueller Report, officially known as the Report on the Investigation into Russian...

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”....

Featured

Growing Data Scientists Into Manager Roles

In this post, Ricky Chachra, Research Science Manager at Lyft, provides insight for companies looking to home-grow their promising individual contributors (ICs) into...

Featured

Model Management and the Era of the Model-Driven Business

Over the past few years, we’ve seen a new community of data science leaders emerge. Regardless of their industry, we have heard three...

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...

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...

Announcing Domino 3.3: Datasets and Experiment Manager

Our mission at Domino is to enable organizations to put models at the heart of their business. Models are so different from software...

Data Science vs Engineering: Tension Points

This blog post provides highlights and a full written transcript from the panel, “Data Science Versus Engineering: Does It Really Have To Be...

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