CIO Business Intelligence

SEPTEMBER 12, 2022

Prioritize time for experimentation. A sure-fire formula for driving innovative growth is to “try something new, learn fast, pivot as needed, and scale success,’’ says Mike Crowe, CIO of Colgate-Palmolive. The team was given time to gather and clean data and experiment with machine learning models,’’ Crowe says.

Experimentation 133

Experimentation Consulting Technology Machine Learning 133

Cloudera

AUGUST 20, 2020

It’s been a year filled with disruption and uncertainty. Acceptance that it will be an experiment — ML really requires a lot of experimentation, and often times you don’t know what’s going to be successful. 2020 may well go down as the year where what seems impossible today, did become possible tomorrow.

Enterprise 120

Enterprise Experimentation Uncertainty Deep Learning 120

The Unofficial Google Data Science Blog

DECEMBER 14, 2021

by AMIR NAJMI & MUKUND SUNDARARAJAN Data science is about decision making under uncertainty. Some of that uncertainty is the result of statistical inference, i.e., using a finite sample of observations for estimation. But there are other kinds of uncertainty, at least as important, that are not statistical in nature.

Uncertainty 56

Uncertainty Statistics Measurement Cost-Benefit 56

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. The AGI would need to handle uncertainty and make decisions with incomplete information.

Cost-Benefit 93

Cost-Benefit Modeling Manufacturing Interactive 93

O'Reilly on Data

SEPTEMBER 15, 2020

Technical competence results in reduced risk and uncertainty. Results are typically achieved through a scientific process of discovery, exploration, and experimentation, and these processes are not always predictable. There’s a lot of overlap between these factors. Conclusion.

Cost-Benefit 293

Cost-Benefit Advertising ROI Machine Learning 293

O'Reilly on Data

OCTOBER 13, 2020

Similarly, in “ Building Machine Learning Powered Applications: Going from Idea to Product ,” Emmanuel Ameisen states: “Indeed, exposing a model to users in production comes with a set of challenges that mirrors the ones that come with debugging a model.”. We can’t wait to see what you build!

Management 244

Management Metrics Machine Learning Modeling 244

Domino Data Lab

JUNE 23, 2019

Pete Skomoroch ’s “ Product Management for AI ”session at Rev provided a “crash course” on what product managers and leaders need to know about shipping machine learning (ML) projects and how to navigate key challenges. Be aware that machine learning often involves working on something that isn’t guaranteed to work.

Management 110

Management Machine Learning Experimentation Metrics 110

The Unofficial Google Data Science Blog

OCTOBER 21, 2015

It is important to make clear distinctions among each of these, and to advance the state of knowledge through concerted observation, modeling and experimentation. Note also that this account does not involve ambiguity due to statistical uncertainty. We sliced and diced the experimental data in many many ways.

Slice and Dice 45

Slice and Dice Experimentation Data-driven Data Science 45

O'Reilly on Data

SEPTEMBER 11, 2019

Machine learning, artificial intelligence, data engineering, and architecture are driving the data space. The Strata Data Conferences helped chronicle the birth of big data, as well as the emergence of data science, streaming, and machine learning (ML) as disruptive phenomena.

IoT 20

IoT Big Data Data Warehouse Uncertainty 20