Smart Data Collective

JUNE 21, 2023

Last summer, we wrote an article about the ways that artificial intelligence is changing video editing software. This frees up time for experimentation and achieving superior results. The market for AI software is booming. Precedence Research States that the market was worth $138 billion last year and it is growing exponentially.

Software 104

Software Technology Experimentation Data-driven 104

Domino Data Lab

JUNE 23, 2019

Skomoroch proposes that managing ML projects are challenging for organizations because shipping ML projects requires an experimental culture that fundamentally changes how many companies approach building and shipping software. The biggest time sink is often around data collection, labeling and cleaning.

Management 110

Management Machine Learning Experimentation Metrics 110

IBM Big Data Hub

MARCH 27, 2024

This article explores an innovative way to streamline the estimation of Scope 3 GHG emissions leveraging AI and Large Language Models (LLMs) to help categorize financial transaction data to align with spend-based emissions factors.

Experimentation 73

Experimentation Deep Learning Modeling Data-driven 73

Ontotext

MAY 18, 2020

Ever since Hippocrates founded his school of medicine in ancient Greece some 2,500 years ago, writes Hannah Fry in her book Hello World: Being Human in the Age of Algorithms , what has been fundamental to healthcare (as she calls it “the fight to keep us healthy”) was observation, experimentation and the analysis of data.

Knowledge Discovery 72

Knowledge Discovery Experimentation Data-driven Metadata 72

Cloudera

FEBRUARY 16, 2022

In this article, we explore model governance, a function of ML Operations (MLOps). We can think of model lineage as the specific combination of data and transformations on that data that create a model. This maps to the data collection, data engineering, model tuning and model training stages of the data science lifecycle.

Machine Learning 85

Machine Learning Modeling Metadata Recreation/Entertainment 85

Dataiku

APRIL 22, 2022

For companies with small datasets and a mandate to move beyond experimentation, Frugal AI promises to be a way to overcome this challenge. Storage infrastructure and data collection/processing costs. Frugal by Design: Why Focus on the Data and Not the Code?

Experimentation 59

Experimentation Business Objectives Modeling Manufacturing 59

Depict Data Studio

JULY 13, 2021

Today’s article comes from Maryfrances Porter, Ph.D. & — Thank you to Ann Emery, Depict Data Studio, and her Simple Spreadsheets class for inviting us to talk to them about the use of statistics in nonprofit program evaluation! Ask us more about Data Teams! You HAVE TO GRAPH Your Data to See How it Looks.

Statistics 98

Statistics Testing Recreation/Entertainment Experimentation 98

Insight

MARCH 27, 2019

Ways to get better data Efforts to improve the quality of data often have a higher return on investment than efforts to enhance models. There are three main ways to improve data: collecting more data, synthesizing new data, or augmenting existing data. 2018 , blog ).

Optimization 75

Optimization Cost-Benefit Modeling Publishing 75

Occam's Razor

OCTOBER 12, 2017

The tiny downside of this is that our parents likely never had to invest as much in constant education, experimentation and self-driven investment in core skills. I read articles they write. There is never a boring moment, there is never time when you can’t do something faster or smarter. I have alerts for them.

Marketing 138

Marketing Analytics Machine Learning Strategy 138

O'Reilly on Data

JULY 28, 2020

In this article, we turn our attention to the process itself: how do you bring a product to market? Without clarity in metrics, it’s impossible to do meaningful experimentation. Experimentation should show you how your customers use your site, and whether a recommendation engine would help the business. Identifying the problem.

Marketing 364

Marketing Experimentation Metrics Testing 364

Domino Data Lab

OCTOBER 2, 2019

This article covers causal relationships and includes a chapter excerpt from the book Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications by Andrew Kelleher and Adam Kelleher. Data collected from this system reflects the way the world works when we just observe it.

Machine Learning 68

Machine Learning Measurement Modeling Testing 68

Domino Data Lab

FEBRUARY 4, 2019

We’ll unpack curiosity as a core attribute of effective data science, look at how that informs process for data science (in contrast to Agile, etc.), and dig into details about where science meets rhetoric in data science. That body of work has much to offer the practice of leading data science teams.

Data Science 66

Data Science Experimentation Machine Learning Data-driven 66

Domino Data Lab

MAY 8, 2019

Paco Nathan’s latest article features several emerging threads adjacent to model interpretability. I’ve been out themespotting and this month’s article features several emerging threads adjacent to the interpretability of machine learning models. Keep in mind that data science is fundamentally interdisciplinary.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Occam's Razor

APRIL 29, 2013

PS: The phrase "real-time data analysis" is an oxymoron. Real-time data is super valuable if zero human beings are involved from data collection to action being taken. Eight Silly Data Things Marketing People Believe That Get Them Fired. PPS: I've mentioned one exception in the past.

Marketing 166

Marketing Metrics Advertising Measurement 166

Insight

MARCH 12, 2020

In this article, I will discuss the construction of the AIgent, from data collection to model assembly. Data Collection The AIgent leverages book synopses and book metadata. The latter is any type of external data that has been attached to a book? Instead, I built the AIgent. In other words, if 0.1%

Modeling 59

Modeling Metadata Publishing Sales 59