Domino Data Lab

APRIL 3, 2019

Also, while surveying the literature two key drivers stood out: Risk management is the thin-edge-of-the-wedge ?for Given those two, plus SQL gaining eminence as a database strategy, a decidedly relational picture coalesced throughout the decade. Andrew Ng later described this strategy as the “Virtuous Cycle of AI” – a.k.a.

Data Governance 84

Data Governance Machine Learning Metadata Big Data 84

Domino Data Lab

JANUARY 6, 2019

What are the projected risks for companies that fall behind for internal training in data science? Skills continuing to grow in prominence by 2022 include analytical thinking and innovation as well as active learning and learning strategies. Wes McKinney (2017). Aurélien Géron (2017). NASA persistently misspells Jupyter.

Data Science 56

Data Science Machine Learning Reporting Visualization 56

Domino Data Lab

MAY 20, 2021

The problem with this approach is that in highly imbalanced sets it can easily lead to a situation where most of the data has to be discarded, and it has been firmly established that when it comes to machine learning data should not be easily thrown out (Banko and Brill, 2001; Halevy et al., In their 2002 paper Chawla et al. Chawla et al.

Machine Learning 136

Machine Learning Metrics Data mining Knowledge Discovery 136

Domino Data Lab

AUGUST 8, 2019

Consider the following timeline: 2001 – Physics grad students are getting hired in quantity by hedge funds to work on Wall St. The probabilistic nature changes the risks and process required. We face problems—crises—regarding risks involved with data and machine learning in production. Public Health Reports (2017-07-10).

Data Science 71

Data Science Machine Learning Data Governance Statistics 71