This Domino Data Science Field Note covers Pete Skomoroch’s recent Strata London talk. It focuses on his ML product management insights and lessons learned. If you are interested in hearing more practical insights on ML or AI product management, then consider attending Pete’s upc … | Continue reading
Our last release, Domino 3.3 saw the addition of two major capabilities: Datasets and Experiment Manager. “Datasets”, a high-performance, revisioned data store offers data scientists the flexibility they need to make use of large data resources when developing models. And “Experi … | Continue reading
This Domino Data Science Field Note provides highlights and excerpted slides from Chloe Mawer’s “The Ingredients of a Reproducible Machine Learning Model” talk at a recent WiMLDS meetup. Mawer is a Principal Data Scientist at Lineage Logistics as well as an Adjunct Lecturer at No … | Continue reading
This Domino Data Science Field Note provides highlights and excerpted slides from Chloe Mawer’s “The Ingredients of a Reproducible Machine Learning Model” talk at a recent WiMLDS meetup. Mawer is a Principal Data Scientist at Lineage Logistics as well as an Adjunct Lecturer at No … | Continue reading
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 Interference in the 2016 Presidentia … | Continue reading
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 Interference in the 2016 Presidentia … | Continue reading
In Paco Nathan's latest column, he explores the role of curiosity in data science work as well as Rev 2, an upcoming summit for data science leaders. Intro | Continue reading
Reflections Before we get too far into 2019, I wanted to take a brief moment to reflect on some of the changes we’ve seen in the market. In 2018 we saw the | Continue reading
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 C | Continue reading
This blog post provides insights on how to use the SHAP and LIME Python libraries in practice and how to interpret their output, helping readers prepare to | Continue reading
In this guest post, Holden Karau, Apache Spark Committer, provides insights on how to create multi-language pipelines with Apache Spark and avoid rewriting | Continue reading
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?” wit | Continue reading
At some point in their careers, almost every data scientist has written code to perform a series of steps, and thought, “It would be great if I could build | Continue reading
Derrick Higgins, AmFam Data Science & Analytics, discusses how Bayesian methods can be applied to improve the quality of annotated training sets. Sessi | Continue reading
This blog post provides a brief technical introduction to the SHAP and LIME Python libraries, followed by code and output to highlight a few pros and cons | Continue reading
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 Goog | Continue reading
This blog post includes candid insights about addressing tension points that arise when people collaborate on developing and deploying models. Domino’s Hea | Continue reading
In this post, Ricky Chachra, Research Science Manager at Lyft, provides insight for companies looking to home-grow their promising individual contributors | Continue reading
This blog post introduces new Domino 3.0 features. | Continue reading
Research, insights, and implications to consider when developing predictive risk assessment models | Continue reading
Covers themes of data science for accountability, reinforcement learning challenges assumptions, as well as surprises within AI and Economics. | Continue reading
Brief Overview of LIME (Local Interpretable Model-Agnostic Explanations) | Continue reading
In this guest blog post, Derrick Higgins, of American Family Insurance, covers item response theory (IRT) and how data scientists can apply it within a pro | Continue reading
Introduction: New Monthly Series! Welcome to a new monthly series! I’ll summarize highlights from recent industry conferences, new open source projects, in | Continue reading
This Domino Data Science Field Note covers a proposed definition of machine learning interpretability, why interpretability matters, and the arguments for | Continue reading
This Domino Data Science Field Note covers a proposed definition of machine learning interpretability, why interpretability matters, and the arguments for | Continue reading
Key highlights from Clare Gollnick’s talk, “The limits of inference: what data scientists can learn from the reproducibility crisis in science”, are covere | Continue reading
This Domino Field Note provides highlights and excerpted slides from Amanda Casari’s “Feature Engineering for Machine Learning” talk at QCon Sao Paulo. Cas | Continue reading
In this guest post, Sean Owen, writes about three data situations that provide ambiguous results and how causation helps clarifies the interpretation of da | Continue reading
In this post, Don Miner covers how to identify, evaluate, prioritize, and pick which data science problems to work on next. Don is a cofounder of Miner | Continue reading
This Domino Data Science Field Note provides highlights and video clips from Addhyan Pandey’s Domino Data Pop-Up talk, “Leveraging Data Science in the Auto | Continue reading
Derrick Higgins of American Family Insurance presented a talk, “Classify all the Things (with multiple labels): The most common type of modeling task no on | Continue reading
In this post, Josh Poduska, Chief Data Scientist at Domino Data Lab, advocates for a common taxonomy of terms within the data science industry. The propose | Continue reading
Wes McKinney, Director of Ursa Labs and creator of pandas project, presented the keynote, "Advancing Data Science Through Open Source" at Rev. McKinney's k | Continue reading
This Domino Data Science Field Note provides some highlights of Alice Zheng’s report, "Evaluating Machine Learning Models", including evaluation metrics fo | Continue reading
Alex Leeds, presented “Building Up Local Models of Customers” at a Domino Data Science Popup. Leeds discussed how the Squarespace data science team built m | Continue reading
This guest blog post from Paco Nathan dives into how people and machines collaborating together to perform work is real and not science fiction. Paco Natha | Continue reading
Kate Crawford discussed bias at a recent SF-based City Arts and Lectures talk and a recording of the discussion will be broadcast, May 6th, on KQED and loc | Continue reading