O'Reilly on Data

DECEMBER 12, 2019

Model debugging is an emergent discipline focused on finding and fixing problems in ML systems. In addition to newer innovations, the practice borrows from model risk management, traditional model diagnostics, and software testing. Interpretable ML models and explainable ML.

Machine Learning 191

Machine Learning Modeling Testing Risk Management 191

Domino Data Lab

JULY 9, 2019

He advocated that an impactful ML solution does not end with Google Slides but becomes “a working API that is hosted or a GUI or some piece of working code that people can put to work” Wiggins also dove into examples of applying unsupervised, supervised, and reinforcement learning to address business problems.

Data Science 112

Data Science Machine Learning Advertising Modeling 112

Jet Global

MAY 1, 2023

Companies like Tableau (which raised over $250 million when it had its IPO in 2013) demonstrated an unmet need in the market. If you host a SaaS application in the cloud, do not simply assess desktop tools or run analysis off a cleansed spreadsheet. Users’ varied needs require a shift in traditional BI thinking.

Analytics 52

Analytics Cost-Benefit Visualization Dashboards 52