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ML (O)Ops: What Data To Collect? (part 3)

The first part of the series, “Improving and Deploying On-Device Models With Confidence,” is posted here. The second part, “Keeping Track of Changes,” is posted here. With Atul Dhingra For a broad class of machine learning problems, nitpicking over the neural net architecture is over (see, for instance, here). Instead,

Market Welfare: Why Are Covid-19 Vaccines Still Underfunded?

“To get roughly 70% of the planet’s population inoculated by April, the IMF calculates, would cost just $50bn. The cumulative economic benefit by 2025, in terms of increased global output, would be $9trn, to say nothing of the many lives that would be saved.” https://www.economist.com/leaders/

The Story of Science: Storytelling Bias in Science

Often enough, scientists are left with the unenviable task of conducting an orchestra with out-of-tune instruments. They are charged with telling a coherent story about noisy results. Scientists defer to the demand partly because there is a widespread belief that a journal article is the appropriate grouping variable at which

The True Ones: Best Guess of True Proportion of 1s

ML models are generally used to make predictions about individual observations. Sometimes, however, the business decision is based on aggregate data. For example, say a company sells pants and wants to know how many will be returned over a certain period. Say the company has an ML model that predicts

ML (O)Ops! Keeping Track of Changes (Part 2)

The first part of the series, “Improving and Deploying On-Device Models With Confidence”, is posted here. With Atul Dhingra One way to automate classification is to compare new instances to a known list and plug in the majority class of the exact match. For such instance-based learning, you often don’

ML (O)Ops! Improving and Deploying On-Device Models With Confidence (Part 1)

With Atul Dhingra. Part 1 of a multi-part series. It is well known that ML Engineers today spend most of their time doing things that do not have a lot to do with machine learning. They spend time working on technically unsophisticated but important things like deployment of models, keeping

To the Better End: How the Middle Can Improve the End

Neil deGrasse Tyson: “…[generational spaceships produce] interesting ethical questions … to bring an entire generation of humans into the world whose only mission is to bring another generation into the world with a goal that they will never see.” Chuck Nice: “In a way, Neil, that is [the] kind of the

Build Software for the Lay User

Most word processing software helpfully point out grammatical errors and spelling mistakes. Some even autocorrect. And some, like Grammarly, even give style advice. Now, consider software used for business statistics. Say you want to compute the correlation between two vectors: [100, 2000, 300, 400, 500, 600] and [1, 2, 3,