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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....

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...

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...

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 Ethics: Contesting Truth and Rearranging Power

This Domino Data Science Field Note covers Chris Wiggins's recent data ethics seminar at Berkeley. The article focuses on 1) proposed frameworks for defining and designing...

Seeking Reproducibility within Social Science: Search and Discovery

Julia Lane, NYU Professor, Economist and cofounder of the Coleridge Initiative, presented “Where’s the Data: A New Approach to Social Science Search & Discovery” at Rev....

Data Science at The New York Times

Chris Wiggins, Chief Data Scientist at The New York Times, presented "Data Science at the New York Times" at Rev. Wiggins advocated that data scientists find...

Announcing Trial and Domino 3.5: Control Center for Data Science Leaders

Even the most sophisticated data science organizations struggle to keep track of their data science projects. Data science leaders want to know, at any given moment,...

Product Management for AI

Pete Skomoroch presented “Product Management for AI” at Rev. This post provides a distilled summary, video, and full transcript. Session Summary Pete Skomoroch’s “Product Management for...

Themes and Conferences per Pacoid, Episode 10

Co-chair Paco Nathan provides highlights of Rev 2, a data science leaders summit. Introduction Welcome back to our monthly burst of themespotting and conference summaries. We...

Machine Learning Product Management: Lessons Learned

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...

Announcing Domino 3.4: Furthering Collaboration with Activity Feed

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...

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