IBM Big Data Hub

SEPTEMBER 6, 2023

AI marketing is the process of using AI capabilities like data collection, data-driven analysis, natural language processing (NLP) and machine learning (ML) to deliver customer insights and automate critical marketing decisions. What is AI marketing?

Marketing 79

Marketing Technology Advertising ROI 79

O'Reilly on Data

MARCH 31, 2020

That foundation means that you have already shifted the culture and data infrastructure of your company. If you’re just learning to walk, there are ways to speed up your progress. Product recommendations are easy; nobody is injured if you recommend products that your customers don’t want, though you won’t see much ROI.

Management 333

Management Machine Learning Experimentation Metrics 333

IBM Big Data Hub

AUGUST 28, 2023

Information retrieval The first step in the text-mining workflow is information retrieval, which requires data scientists to gather relevant textual data from various sources (e.g., The data collection process should be tailored to the specific objectives of the analysis.

Data mining 53

Data mining Machine Learning Deep Learning Marketing 53

IBM Big Data Hub

FEBRUARY 14, 2024

By infusing AI into IT operations , companies can harness the considerable power of NLP, big data, and ML models to automate and streamline operational workflows, and monitor event correlation and causality determination. AIOps is one of the fastest ways to boost ROI from digital transformation investments.

Cost-Benefit 83

Cost-Benefit Insurance Unstructured Data Machine Learning 83

Domino Data Lab

MARCH 3, 2019

Then, when we received 11,400 responses, the next step became obvious to a duo of data scientists on the receiving end of that data collection. Over the past six months, Ben Lorica and I have conducted three surveys about “ABC” (AI, Big Data, Cloud) adoption in enterprise. One-fifth use reinforcement learning.

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49

Domino Data Lab

MAY 8, 2019

The lens of reductionism and an overemphasis on engineering becomes an Achilles heel for data science work. Instead, consider a “full stack” tracing from the point of data collection all the way out through inference. Machine learning model interpretability. training data”) show the tangible outcomes.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Domino Data Lab

JUNE 23, 2019

It used deep learning to build an automated question answering system and a knowledge base based on that information. It is like the Google knowledge graph with all those smart, intelligent cards and the ability to create your own cards out of your own data. People want to just dip their toes in and do a small sample project.

Management 110

Management Machine Learning Experimentation Metrics 110