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 349

Enterprise Deep Learning Data Governance Risk 349

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 363

Management Machine Learning Experimentation Metrics 363

CIO Business Intelligence

SEPTEMBER 30, 2024

Emphasizing ethics and impact Like many of the government agencies it serves, Mathematica started its cloud journey on AWS shortly after Bell arrived six years ago and built the Mquiry data collection, collaboration, management, and analytics platform on the Mathematica Cloud Support System for its myriad clients.

Data Quality 116

Data Quality Experimentation Insurance Statistics 116

datapine

JANUARY 31, 2022

It’s all about using data to get a clearer understanding of reality so that your company can make more strategically sound decisions (instead of relying only on gut instinct or corporate inertia). Ultimately, business intelligence and analytics are about much more than the technology used to gather and analyze data.

Business Intelligence 214

Business Intelligence Analytics Cost-Benefit ROI 214

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

Management 110

Management Machine Learning Experimentation Metrics 110

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 364

Marketing Experimentation Metrics Testing 364