Domino Data Lab

MAY 8, 2019

The lens of reductionism and an overemphasis on engineering becomes an Achilles heel for data science work. Instead, consider a “full stack” tracing from the point of data collection all the way out through inference. Finale Doshi-Velez, Been Kim (2017-02-28) ; see also the Domino blog article about TCAV. 2018-06-21).

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Domino Data Lab

AUGUST 1, 2021

Skater provides a wide range of algorithms that can be used for visual interpretation (e.g. but it generally relies on measuring the entropy in the change of predictions given a perturbation of a feature. In IJCAI 2017 Workshop on Explainable Artificial Intelligence (XAI), pages 24–30, Melbourne, Australia, 2017.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139

Ontotext

MAY 18, 2020

The capacity and performance of supercomputers is measured with the so-called FLOPS (floating point operations per second). As of 2017, the fastest computers have reached a speed of 93 PetaFLOPS, which is: 93×1015, or 93,000,000,000,000,000 operations per second. There are four types of data sources that the team will work with.

Knowledge Discovery 72

Knowledge Discovery Experimentation Data-driven Metadata 72

Domino Data Lab

MAY 20, 2021

Working with highly imbalanced data can be problematic in several aspects: Distorted performance metrics — In a highly imbalanced dataset, say a binary dataset with a class ratio of 98:2, an algorithm that always predicts the majority class and completely ignores the minority class will still be 98% correct. Machine Learning, 57–78.

Machine Learning 136

Machine Learning Metrics Data mining Knowledge Discovery 136

Alation

AUGUST 26, 2021

Data intelligence first emerged to support search & discovery, largely in service of analyst productivity. For years, analysts in enterprises had struggled to find the data they needed to build reports. This problem was only exacerbated by explosive growth in data collection and volume. HBR Review May/June 2017.

Metadata 64

Metadata Data Governance Dashboards Software 64

Domino Data Lab

OCTOBER 23, 2019

As a result, there has been a recent explosion in individual statistics that try to measure a player’s impact. The first step to collecting all of the data is to figure out which data source to collect first, and where to get it. R has yet one more way to visualize these relationships in multiple dimensions.

Statistics 59

Statistics Machine Learning Testing Modeling 59

Domino Data Lab

FEBRUARY 4, 2019

People who attended JupyterCon 2017–2018 can attest, an “industry poster session” includes an open bar, catered hors d’oeuvres, lots of mingling … to paraphrase feedback from JupyterCon, “As a tech person, would I get up extra early to meet strangers for coffee at 8:00 am? The ability to measure results (risk-reducing evidence).

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66