Latest

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 This Way?” with Amy Heineike, Paco Nathan, and Pete Warden at Domino HQ. Topics...

Themes and Conferences per Pacoid, Episode 4

Paco Nathan's latest column covers themes that include data privacy, machine ethics, and yes, Don Quixote. Introduction Welcome back to our monthly series...

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...

On the Importance of Community-Led Open Source

Wes McKinney, Director of Ursa Labs and creator of pandas project, presented the keynote, "Advancing Data Science Through Open Source" at Rev. McKinney's...

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...

Code

Making PySpark Work with spaCy: Overcoming Serialization Errors

In this guest post, Holden Karau, Apache Spark Committer, provides insights on how to use spaCy to process text data. Karau is a Developer Advocate at...

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

Managing Data Science as a Capability

Nick Elprin, CEO at Domino, presented a 3-hour training workshop, “Managing Data Science in the Enterprise”, that provided practical insights and interactive breakouts....

Practical Techniques

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...

Building a Domino Web App with Dash

Randi R. Ludwig, Data Scientist at Dell EMC and an organizer of Women in Data Science ATX, covers how to build a Domino...

Leaders at Work

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...

Measuring Data Science Business Value

This blog post covers metrics that help data science leaders ensure their team’s work is aligned to business value. Data science managers and...

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...

Model Evaluation

This Domino Data Science Field Note provides some highlights of Alice Zheng’s report, "Evaluating Machine Learning Models", including evaluation metrics for supervised learning...

Domino 3.0: New Features and User Experiences to Help the World Run on Models

This blog post introduces new Domino 3.0 features. Akansh Murthy is a Technical Product Manager at Domino and previously worked as a software...

Docker, but for Data

Aneesh Karve, Co-founder and CTO of Quilt, visited the Domino MeetUp to discuss the evolution of data infrastructure. This blog post provides a...