O'Reilly on Data

JULY 28, 2020

Without clarity in metrics, it’s impossible to do meaningful experimentation. AI PMs must ensure that experimentation occurs during three phases of the product lifecycle: Phase 1: Concept During the concept phase, it’s important to determine if it’s even possible for an AI product “ intervention ” to move an upstream business metric.

Marketing 362

Marketing Experimentation Metrics Testing 362

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 332

Management Machine Learning Experimentation Metrics 332

O'Reilly on Data

MARCH 18, 2020

It seems as if the experimental AI projects of 2019 have borne fruit. By contrast, AI adopters are about one-third more likely to cite problems with missing or inconsistent data. The logic in this case partakes of garbage-in, garbage out : data scientists and ML engineers need quality data to train their models.

Enterprise 311

Enterprise Deep Learning Data Governance Risk 311

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. The biggest time sink is often around data collection, labeling and cleaning.

Management 110

Management Machine Learning Experimentation Metrics 110

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

Occam's Razor

AUGUST 13, 2012

The questions reveal a bunch of things we used to worry about, and continue to, like data quality and creating data driven cultures. Dealing with data quality doubt is every day and, sadly, very complex challenge for many, if not most, of us. They also reveal things that starting to become scary (Privacy!

Modeling 124

Modeling Metrics Data Quality Data-driven 124

Occam's Razor

DECEMBER 12, 2016

Data quality plays a role into this. And, most of the time, regardless of the size of the size of the company, you only know your code is not working post-launch when data is flowing in (not!). You got me, I am ignoring all the data layer and custom stuff! All that is great.

Analytics 141

Analytics Data-driven Strategy Consulting 141