Tag: Model Management

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....

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...

Themes and Conferences per Pacoid, Episode 9

Paco Nathan's latest article features several emerging threads adjacent to model interpretability. Introduction Welcome back to our monthly burst of themes and conferences. Several technology conferences...

Addressing Irreproducibility in the Wild

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...

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 excerpt provides data...

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 — e.g., they...

Themes and Conferences per Pacoid, Episode 7

Paco Nathan covers recent research on data infrastructure as well as adoption of machine learning and AI in the enterprise. Introduction Welcome back to our monthly...

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 Concept Activation Vectors...

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...

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