O'Reilly on Data

MARCH 31, 2020

Machine learning adds uncertainty. Underneath this uncertainty lies further uncertainty in the development process itself. There are strategies for dealing with all of this uncertainty–starting with the proverb from the early days of Agile: “ do the simplest thing that could possibly work.”

Management 332

Management Machine Learning Experimentation Metrics 332

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

APRIL 8, 2013

We are far too enamored with data collection and reporting the standard metrics we love because others love them because someone else said they were nice so many years ago. They might deal with uncertainty, but they're not random. Online, offline or nonline. Yet this structure rarely exists in companies.

Metrics 156

Metrics KPI Analytics Key Performance Indicator 156

DataRobot Blog

OCTOBER 4, 2022

In the last few years, businesses have experienced disruptions and uncertainty on an unprecedented scale. The situation is even more challenging for companies in industries that use historical data to give them visibility into future operations, staffing, and sales forecasting. Managing Through Socio-Economic Disruption.

Forecasting 59

Forecasting Sales Data-driven Modeling 59

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