The Unofficial Google Data Science Blog

APRIL 23, 2024

Experiments, Parameters and Models At Youtube, the relationships between system parameters and metrics often seem simple — straight-line models sometimes fit our data well. To find optimal values of two parameters experimentally, the obvious strategy would be to experiment with and update them in separate, sequential stages.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

CIO Business Intelligence

MAY 16, 2023

It surpasses blockchain and metaverse projects, which are viewed as experimental or in the pilot stage, especially by established enterprises. higher [in 2022] than in 2017.” Big Data collection at scale is increasing across industries, presenting opportunities for companies to develop AI models and leverage insights from that data.

Advertising 87

Advertising Recreation/Entertainment Experimentation Enterprise 87

Domino Data Lab

JULY 2, 2019

Paco Nathan ‘s latest article covers program synthesis, AutoPandas, model-driven data queries, and more. In other words, using metadata about data science work to generate code. In this case, code gets generated for data preparation, where so much of the “time and labor” in data science work is concentrated.

Metadata 105

Metadata Machine Learning Data Science Data-driven 105

Domino Data Lab

MAY 8, 2019

For example, common practices for collecting data to build training datasets tend to throw away valuable information along the way. The lens of reductionism and an overemphasis on engineering becomes an Achilles heel for data science work. Finale Doshi-Velez, Been Kim (2017-02-28) ; see also the Domino blog article about TCAV.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Domino Data Lab

FEBRUARY 4, 2019

In Paco Nathan ‘s latest column, he explores the role of curiosity in data science work as well as Rev 2 , an upcoming summit for data science leaders. Welcome back to our monthly series about data science. and dig into details about where science meets rhetoric in data science. Introduction. This is not that.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

O'Reilly on Data

MARCH 23, 2023

What is it, how does it work, what can it do, and what are the risks of using it? All of these models are based on a technology called Transformers , which was invented by Google Research and Google Brain in 2017. Unlike labels, embeddings are learned from the training data, not produced by humans.

IT 262

IT Modeling Testing Risk 262

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. The term “AI,” meanwhile, is No.

IoT 20

IoT Big Data Data Warehouse Uncertainty 20