Domino Data Science Blog

Eduardo Ariño de la Rubia

Eduardo Ariño de la Rubia is a lifelong technologist with a passion for data science who thrives on effectively communicating data-driven insights throughout an organization. A student of negotiation, conflict resolution, and peace building, Ed is focused on building tools that help humans work with humans to create insights for humans.

Data Science

Improving Zillow's Zestimate with 36 Lines of Code

Zillow and Kaggle recently started a $1 million competition to improve the Zestimate. We used H2O’s AutoML to generate a solution.

By Eduardo Ariño de la Rubia3 min read

Data Science

Horizontal Scaling for Parallel Experimentation

The amount of time data scientists spend waiting for experiment results is the difference between making incremental improvements and making significant advances. With parallel experimentation, data scientists can run more experiments faster, leaving more time to try novel and unorthodox approaches—the kind that leads to exponential improvements and discoveries.

By Eduardo Ariño de la Rubia6 min read

Data Science

Multicore Data Science with R and Python

This post shows a number of different package and approaches for leveraging parallel processing with R and Python.

By Eduardo Ariño de la Rubia16 min read

Product Updates

Git Integration in Domino

We recently released new functionality that provides first-class integration between Domino and git. This post describes the new feature, and describes our perspective on the unique requirements of version control in the context of data science—as distinct from software engineering—workflows.

By Eduardo Ariño de la Rubia5 min read

Data Science

The Cost of Doing Data Science on Laptops

At the heart of the data science process are the resource intensive tasks of modeling and validation. During these tasks, data scientists will try and discard thousands of temporary models to find the optimal configuration. Even for small data sets, this could take hours to process.

By Eduardo Ariño de la Rubia6 min read

Data Science

Benchmarking Predictive Models

It's been said that debugging is harder than programming. If we, as data scientists, are developing models ("programming") at the limits of our understanding, then we're probably not smart enough to validate those models (“debug”) effectively.

By Eduardo Ariño de la Rubia13 min read

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