O'Reilly on Data

JULY 28, 2020

It’s often difficult for businesses without a mature data or machine learning practice to define and agree on metrics. Fair warning: if the business lacks metrics, it probably also lacks discipline about data infrastructure, collection, governance, and much more.) Agreeing on metrics. Is it a problem that should be solved?

Marketing 362

Marketing Experimentation Metrics Testing 362

Dataiku

JANUARY 29, 2020

If you're a business, do you really want to bend over backwards to get 99 percent accuracy with a machine learning model when a simple linear regression that gets you 94 percent accuracy gets the job done?

Modeling 11

Modeling Data Science Machine Learning 11

IBM Big Data Hub

AUGUST 15, 2022

Sometimes as data scientists, we are often so determined to build a perfect model that we can unintentionally include human bias into our models. Often the bias creeps in through training data and then is amplified and embedded in the model. Data risk assessment.

Data mining 62

Data mining Risk Modeling Risk Management 62

Domino Data Lab

JANUARY 6, 2019

In Paco Nathan ‘s latest column, he explores the theme of “learning data science” by diving into education programs, learning materials, educational approaches, as well as perceptions about education. He is also the Co-Chair of the upcoming Data Science Leaders Summit, Rev. Learning Data Science.

Data Science 56

Data Science Machine Learning Reporting Visualization 56

CIO Business Intelligence

MAY 26, 2022

Is there a model that can provide the necessary results? But the tools that data scientists use to create these proofs of concept often don’t translate well into production systems. As a result, it can take more than nine months on average to deploy an AI or ML solution, according to IDC data. “We How can it be built?

Machine Learning 145

Machine Learning Modeling Data-driven Enterprise 145

CIO Business Intelligence

MAY 28, 2022

Is there a model that can provide the necessary results? But the tools that data scientists use to create these proofs of concept often don’t translate well into production systems. As a result, it can take more than nine months on average to deploy an AI or ML solution, according to IDC data. “We How can it be built?

Machine Learning 139

Machine Learning Modeling Data-driven Enterprise 139

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.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66