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. AI can help marketers create and optimize content to meet the new standards.

Marketing 83

Marketing Technology Advertising ROI 83

bridgei2i

MARCH 9, 2021

Machine Learning: Since intelligence without learning isn’t intelligence, this subset of AI focuses on parsing data and modifying itself without human effort. ML techniques provide better data-based outputs over time. Deep Learning: DL falls under ML, but its capabilities aren’t comparable.

Technology 52

Technology Deep Learning Data-driven Optimization 52

Occam's Razor

MARCH 30, 2017

People tend to use these phrases almost interchangeably: Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning. Deep Learning is a specific ML technique. Most Deep Learning methods involve artificial neural networks, modeling how our bran works. There won’t be any need for them.

Marketing Analytics 166

Marketing Analytics Marketing Analytics Deep Learning 166

Domino Data Lab

AUGUST 1, 2021

The interest in interpretation of machine learning has been rapidly accelerating in the last decade. This can be attributed to the popularity that machine learning algorithms, and more specifically deep learning, has been gaining in various domains. Methods for explaining Deep Learning.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139

IBM Big Data Hub

AUGUST 11, 2023

Because ML is becoming more integrated into daily business operations, data science teams are looking for faster, more efficient ways to manage ML initiatives, increase model accuracy and gain deeper insights. MLOps is the next evolution of data analysis and deep learning. MLOps and IBM Watsonx.ai

Data Science 56

Data Science Machine Learning Cost-Benefit Deep Learning 56

IBM Big Data Hub

FEBRUARY 23, 2024

Machine learning (ML) and deep learning (DL) form the foundation of conversational AI development. It signifies a shift in human-digital interaction, offering enterprises innovative ways to engage with their audience, optimize operations, and further personalize their customer experience. billion by 2030.

Enterprise 103

Enterprise Interactive Data-driven Key Performance Indicator 103

DataRobot

OCTOBER 20, 2021

The flow of data through an organization: Mapping how data flows through an organization helps organizations get and stay aligned on potential bias risks with data collection and data degradation. rule-based AI , machine learning , deep learning , etc.)

Machine Learning 52

Machine Learning Metrics Risk Deep Learning 52

Decision Management Solutions

FEBRUARY 8, 2021

As you’ll see, the development of this amazing, one-of-a-kind vessel led to a conclusion that we at Decision Management Solutions see every day in our client work: It’s never enough to just rely on artificial intelligence (AI)/machine learning (ML) to do all the decision-making.

Machine Learning 93

Machine Learning Testing Deep Learning Optimization 93

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. AI platforms can use machine learning and deep learning to spot suspicious or anomalous transactions.

Cost-Benefit 86

Cost-Benefit Insurance Unstructured Data Machine Learning 86

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 56

Data mining Machine Learning Deep Learning Marketing 56

O'Reilly on Data

JUNE 18, 2019

Data programming. Increasing the quality of the available data via either unification or cleaning, or both, is definitely an important and a promising way forward to leverage enterprise data assets. An important paradigm for solving both these problems is the concept of data programming.

Machine Learning 259

Machine Learning Data Quality Statistics Modeling 259

Cloudera

FEBRUARY 16, 2022

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. So, we have workspaces, projects and sessions in that order.

Machine Learning 82

Machine Learning Modeling Metadata Recreation/Entertainment 82

O'Reilly on Data

MARCH 31, 2020

At measurement-obsessed companies, every part of their product experience is quantified and adjusted to optimize user experience. These companies eventually moved beyond using data to inform product design decisions. That foundation means that you have already shifted the culture and data infrastructure of your company.

Management 332

Management Machine Learning Experimentation Metrics 332

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

Insight

JANUARY 14, 2020

In short, I was faced with two major difficulties regarding data collection: I didn’t have nearly enough images, and the images I did have were not representative of a realistic gym environment. Choosing your loss function and optimizer Finally, in the last block of code, we must compile the model that we just built.

Metrics 58

Metrics Optimization Modeling Testing 58

IBM Big Data Hub

MAY 17, 2023

They use drones for tasks as simple as aerial photography or as complex as sophisticated data collection and processing. It can offer data on demand to different business units within an organization, with the help of various sensors and payloads. In a nutshell, DaaS provides simplified asset and value optimization.

Data Collection 80

Data Collection Visualization Data-driven Unstructured Data 80

IBM Big Data Hub

NOVEMBER 14, 2023

Data monetization is not narrowly “selling data sets ;” it is about improving work and enhancing business performance by better-using data. External monetization opportunities enable different types of data in different formats to be information assets that can be sold or have their value recorded when used.

Strategy 110

Strategy Cost-Benefit Data-driven Measurement 110

Domino Data Lab

JULY 22, 2019

We’ve got this complex landscape, tons of data sharing, an economy of data, external data, tons of mobile devices. and drop your deep learning model resource footprint by 5-6 orders of magnitude and run it on devices that don’t even have batteries. You can take TensorFlow.js

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

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.

Management 110

Management Machine Learning Experimentation Metrics 110

datapine

NOVEMBER 19, 2019

There are a large number of tools used in AI, including versions of search and mathematical optimization, logic, methods based on probability and economics, and many others. An exemplary application of this trend would be Artificial Neural Networks (ANN) – the predictive analytics method of analyzing data.

Technology 279

Technology Internet of Things IT IoT 279

CIO Business Intelligence

DECEMBER 15, 2023

Smart maintenance: Connected sensors can help city crews optimize maintenance activities such as garbage pickup, street cleaning, and snow removal to reduce costs and traffic impacts.

Testing 75

Testing Deep Learning Optimization Data Collection 75

Occam's Razor

OCTOBER 12, 2017

Optimal Starting SCOTUS Starting Points. If you would like to pursue my personal strategy, here are a collection of cases to use as starting points. Intro to Machine Learning. Machine Learning. Deep Learning. A good example of this Justice Scalia's opinion in Gonzales v. Back to the whiteboard.

Marketing 136

Marketing Analytics Machine Learning Strategy 136