Rocket-Powered Data Science

FEBRUARY 15, 2023

First, don’t do something just because everyone else is doing it – there needs to be a valid business reason for your organization to be doing it, at the very least because you will need to explain it objectively to your stakeholders (employees, investors, clients). Test early and often. Expect continuous improvement.

Strategy 290

Strategy Experimentation Uncertainty Machine Learning 290

O'Reilly on Data

JULY 28, 2020

Without clarity in metrics, it’s impossible to do meaningful experimentation. There’s a substantial literature about ethics, data, and AI, so rather than repeat that discussion, we’ll leave you with a few resources. Ongoing monitoring of critical metrics is yet another form of experimentation.

Marketing 361

Marketing Experimentation Metrics Testing 361

CIO Business Intelligence

MARCH 7, 2023

While many organizations are successful with agile and Scrum, and I believe agile experimentation is the cornerstone of driving digital transformation, there isn’t a one-size-fits-all approach. Here are some force-multiplying differences achievable by agile data teams: Want that dashboard, then update the data catalog.

Digital Transformation 141

Digital Transformation Data Governance Experimentation Machine Learning 141

Rocket-Powered Data Science

JULY 6, 2021

2) MLOps became the expected norm in machine learning and data science projects. MLOps takes the modeling, algorithms, and data wrangling out of the experimental “one off” phase and moves the best models into deployment and sustained operational phase.

Data-driven 279

Data-driven Machine Learning IoT Deep Learning 279

IBM Big Data Hub

OCTOBER 20, 2023

AI platforms offer a wide range of capabilities that can help organizations streamline operations, make data-driven decisions, deploy AI applications effectively and achieve competitive advantages. Apart from pricing, there are numerous other factors to consider when evaluating the best AI platforms for your business.

Machine Learning 73

Machine Learning Manufacturing Cost-Benefit Deep Learning 73

The Unofficial Google Data Science Blog

JANUARY 14, 2016

by AMIR NAJMI Running live experiments on large-scale online services (LSOS) is an important aspect of data science. We must therefore maintain statistical rigor in quantifying experimental uncertainty. In this post we explore how and why we can be “ data-rich but information-poor ”. And an LSOS is awash in data, right?

Experimentation 41

Experimentation Statistics Metrics Measurement 41