Tag: Model Development
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...
Themes and Conferences per Pacoid, Episode 11
Paco Nathan's latest article covers program synthesis, AutoPandas, model-driven data queries, and more. Introduction Welcome back to our monthly burst of themespotting and conference summaries. BTW,...
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...
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...
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 Science: Start Writing...
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...
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 Content sat down...