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 363

Marketing Experimentation Metrics Testing 363

O'Reilly on Data

MARCH 31, 2020

You’re responsible for the design, the product-market fit, and ultimately for getting the product out the door. Pragmatically, machine learning is the part of AI that “works”: algorithms and techniques that you can implement now in real products. We won’t go into the mathematics or engineering of modern machine learning here.

Management 339

Management Machine Learning Experimentation Metrics 339

IBM Big Data Hub

APRIL 18, 2024

LLMs like ChatGPT are trained on massive amounts of text data, allowing them to recognize patterns and statistical relationships within language. Here are some areas where organizations are seeing a ROI: Text (83%) : Gen AI assists with automating tasks like report writing, document summarization and marketing copy generation.

Cost-Benefit 89

Cost-Benefit Modeling Manufacturing Interactive 89

O'Reilly on Data

MARCH 18, 2020

Supervised learning is the most popular ML technique among mature AI adopters, while deep learning is the most popular technique among organizations that are still evaluating AI. It seems as if the experimental AI projects of 2019 have borne fruit. Supervised learning is dominant, deep learning continues to rise.

Enterprise 318

Enterprise Deep Learning Data Governance Risk 318

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. It used deep learning to build an automated question answering system and a knowledge base based on that information.

Management 110

Management Machine Learning Experimentation Metrics 110

Domino Data Lab

MAY 8, 2019

For example, in the case of more recent deep learning work, a complete explanation might be possible: it might also entail an incomprehensible number of parameters. They also require advanced skills in statistics, experimental design, causal inference, and so on – more than most data science teams will have.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105