O'Reilly on Data

MARCH 31, 2020

Because it’s so different from traditional software development, where the risks are more or less well-known and predictable, AI rewards people and companies that are willing to take intelligent risks, and that have (or can develop) an experimental culture. Measurement, tracking, and logging is less of a priority in enterprise software.

Management 332

Management Machine Learning Experimentation Metrics 332

Domino Data Lab

FEBRUARY 4, 2019

We’ll unpack curiosity as a core attribute of effective data science, look at how that informs process for data science (in contrast to Agile, etc.), and dig into details about where science meets rhetoric in data science. That body of work has much to offer the practice of leading data science teams.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

Domino Data Lab

JUNE 23, 2019

Skomoroch proposes that managing ML projects are challenging for organizations because shipping ML projects requires an experimental culture that fundamentally changes how many companies approach building and shipping software. These measurement-obsessed companies have an advantage when it comes to AI.

Management 110

Management Machine Learning Experimentation Metrics 110

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. measure the subjects’ ability to trust the models’ results. training data”) show the tangible outcomes.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Occam's Razor

AUGUST 13, 2012

Yehoshua Coren: Best ways to measure user behavior in a multi-touch, multi-device digital world. What's possible to measure. What's not possible to measure. Bjoern Sjut3: My main issue at the moment: How will multi-channel funnels and ROI calculations work in a multi device world? Let's do this!

Modeling 124

Modeling Metrics Data Quality Data-driven 124

Occam's Razor

MARCH 23, 2010

Too many new things are happening too fast and those of us charged with measuring it have to change the wheels while the bicycle is moving at 30 miles per hour (and this bicycle will become a car before we know it – all while it keeps moving, ever faster). . ~ It has simply not had a break to catch a breath and mature. Likely not.

Analytics 118

Analytics Consulting Metrics Experimentation 118

Occam's Razor

OCTOBER 19, 2010

If after rigorous analysis you have determined that you have evolved to a stage that you need a data warehouse then you are out of luck with Yahoo! If you can show ROI on a DW it would be a good use of your money to go with Omniture Discover, WebTrends Data Mart, Coremetrics Explore. and Google, get a paid solution. Three tools.

Analytics 135

Analytics Testing Measurement Optimization 135