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. If you can’t walk, you’re unlikely to run.

Management 338

Management Machine Learning Experimentation Metrics 338

Domino Data Lab

MAY 15, 2019

Pete indicates, in both his November 2018 and Strata London talks, that ML requires a more experimental approach than traditional software engineering. It is more experimental because it is “an approach that involves learning from data instead of programmatically following a set of human rules.”

Machine Learning 111

Machine Learning Management Experimentation Uncertainty 111

Occam's Razor

MARCH 23, 2010

Having two tools guarantees you are going to be data collection, data processing and data reconciliation organization. It is possible that you'll be the exception and build the first clickstream data warehouse where you'll deliver positive ROI (against the Total Cost of Ownership ). Likely not.

Analytics 119

Analytics Consulting Metrics Experimentation 119

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 136

Analytics Testing Measurement Optimization 136

Occam's Razor

AUGUST 13, 2012

Bjoern Sjut3: My main issue at the moment: How will multi-channel funnels and ROI calculations work in a multi device world? If your wish in the second part is to track effectiveness of advertising ( how to determine ROI ) then please see this post: Measuring Incrementality: Controlled Experiments to the Rescue! That is the solution.

Modeling 125

Modeling Metrics Data Quality Data-driven 125

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. And then you’ll do a lot of work to get it out and then there’ll be no ROI at the end.

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. Keep in mind that data science is fundamentally interdisciplinary. Let’s look through some antidotes.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105