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

CIO Business Intelligence

NOVEMBER 15, 2022

Experiment with the “highly visible and highly hyped”: Gartner repeatedly pointed out that organisations that innovate during tough economic times “stay ahead of the pack”, with Mesaglio in particular calling for such experimentation to be public and visible. Artificial Intelligence, Digital Transformation, Innovation, Machine Learning

Digital Transformation 143

Digital Transformation Machine Learning Experimentation Data-driven 143

The Unofficial Google Data Science Blog

APRIL 23, 2024

Crucially, it takes into account the uncertainty inherent in our experiments. To find optimal values of two parameters experimentally, the obvious strategy would be to experiment with and update them in separate, sequential stages. In this section we’ll discuss how we approach these two kinds of uncertainty with QCQP.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

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 121

Enterprise Experimentation Uncertainty Deep Learning 121

CIO Business Intelligence

SEPTEMBER 6, 2023

The use of AI-generated code is still in an experimental phase for many organizations due to numerous uncertainties such as its impact on security, data privacy, copyright, and more. To learn more, visit us here. Artificial Intelligence, Machine Learning

Software 119

Software Risk Experimentation Uncertainty 119

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

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

AWS Big Data

SEPTEMBER 5, 2024

Intuitively, for some extremely short user inputs, the vectors generated by dense vector models might have significant semantic uncertainty, where overlaying with a sparse vector model could be beneficial. Experimental data selection For retrieval evaluation, we used to use the datasets from BeIR.

Metrics 111

Metrics Testing Experimentation Modeling 111

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 89

Cost-Benefit Manufacturing Modeling Interactive 89

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 46

Slice and Dice Experimentation Data-driven Data Science 46

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 25

IoT Big Data Data Warehouse Uncertainty 25

CIO Business Intelligence

JANUARY 23, 2025

And while its beyond the scope of this article, the applicable knowledge gained through our hands-on experimentation with genAI was head and shoulders above simple web searches (e.g., He specializes in removing fear, uncertainty, and doubt from strategic decision-making through empirical data and market sensing.

Experimentation 116

Experimentation Uncertainty Data-driven Digital Transformation 116

CIO Business Intelligence

APRIL 16, 2025

Innovator/experimenter: enterprise architects look for new innovative opportunities to bring into the business and know how to frame and execute experiments to maximize the learnings.

Enterprise 124

Enterprise Strategy Risk Business Objectives 124

O'Reilly on Data

APRIL 15, 2025

Instead of committing to specific outcomes, they commit to a cadence of experimentation, learning, and iteration. This approach gives stakeholders clear decision points while acknowledging the inherent uncertainty in AI development. But heres my experimentation roadmap. I said, Its uncertain whether well meet that goal.

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Experimentation Testing Metrics Measurement 328