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

Corinium

JUNE 6, 2019

A properly set framework will ensure quality, timeliness, scalability, consistency, and industrialization in measuring and driving the return on investment. It is also important to have a strong test and learn culture to encourage rapid experimentation. Build multiple MVPs to test conceptually and learn from early user feedback.

Insurance 250

Insurance Analytics Forecasting Deep Learning 250

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 333

Management Machine Learning Experimentation Metrics 333

Data Science and Beyond

DECEMBER 23, 2018

In my opinion it’s more exciting and relevant to everyday life than more hyped data science areas like deep learning. However, I’ve found it hard to apply what I’ve learned about causal inference to my work. I’ve been interested in the area of causal inference in the past few years.

Deep Learning 103

Deep Learning Experimentation Data-driven Data Science 103

Cloudera

FEBRUARY 16, 2022

blueberry spacing) is a measure of the model’s interpretability. Support for multiple sessions within a project allows data scientists, engineers and operations teams to work independently alongside each other on experimentation, pipeline development, deployment and monitoring activities in parallel. Model Visibility.

Machine Learning 87

Machine Learning Modeling Metadata Recreation/Entertainment 87

IBM Big Data Hub

APRIL 18, 2024

While leaders have some reservations about the benefits of current AI, organizations are actively investing in gen AI deployment, significantly increasing budgets, expanding use cases, and transitioning projects from experimentation to production.

Cost-Benefit 90

Cost-Benefit Modeling Manufacturing Interactive 90

O'Reilly on Data

OCTOBER 19, 2021

ML apps need to be developed through cycles of experimentation: due to the constant exposure to data, we don’t learn the behavior of ML apps through logical reasoning but through empirical observation. Not only is data larger, but models—deep learning models in particular—are much larger than before.

IT 347

IT Testing Experimentation Software 347