IBM Big Data Hub

DECEMBER 5, 2023

With the rise of highly personalized online shopping, direct-to-consumer models, and delivery services, generative AI can help retailers further unlock a host of benefits that can improve customer care, talent transformation and the performance of their applications.

Unstructured Data 95

Unstructured Data Cost-Benefit Experimentation Machine Learning 95

CIO Business Intelligence

AUGUST 11, 2023

How natural language processing works NLP leverages machine learning (ML) algorithms trained on unstructured data, typically text, to analyze how elements of human language are structured together to impart meaning. Transformer models take applications such as language translation and chatbots to a new level.

Unstructured Data 142

Unstructured Data Modeling Machine Learning Data mining 142

Analytics Vidhya

OCTOBER 7, 2020

This article was published as a part of the Data Science Blogathon. The post Boost Model Accuracy of Imbalanced COVID-19 Mortality Prediction Using GAN-based Oversampling Technique appeared first on Analytics Vidhya. Introduction The article covers the use of Generative Adversarial Networks (GAN), an.

Modeling 296

Modeling Data Science Publishing Analytics 296

Smart Data Collective

AUGUST 28, 2021

Usually, business or data analysts need to extract insights for reporting purposes, so data warehouses are more suitable for them. On the other hand, a data scientist may require access to unstructured data to detect patterns or build a deep learning model, which means that a data lake is a perfect fit for them.

Data Lake 140

Data Lake Data Warehouse Unstructured Data Structured Data 140

Analytics Vidhya

SEPTEMBER 7, 2020

Introduction Overfitting or high variance in machine learning models occurs when the accuracy of your training dataset, the dataset used to “teach” the model, The post How to Treat Overfitting in Convolutional Neural Networks appeared first on Analytics Vidhya.

Machine Learning 291

Machine Learning Modeling Analytics Unstructured Data 291

CIO Business Intelligence

JUNE 7, 2022

But only in recent years, with the growth of the web, cloud computing, hyperscale data centers, machine learning, neural networks, deep learning, and powerful servers with blazing fast processors, has it been possible for NLP algorithms to thrive in business environments. An Industry Redefining Itself.

Unstructured Data 98

Unstructured Data Deep Learning Insurance Interactive 98

IBM Big Data Hub

FEBRUARY 14, 2024

Using machine learning (ML), AI can understand what customers are saying as well as their tone—and can direct them to customer service agents when needed. These systems can evaluate vast amounts of data to uncover trends and patterns, and to make decisions.

Cost-Benefit 90

Cost-Benefit Insurance Unstructured Data Machine Learning 90

IBM Big Data Hub

JULY 6, 2023

To keep up with the pace of consumer expectations, companies are relying more heavily on machine learning algorithms to make things easier. How do artificial intelligence, machine learning, deep learning and neural networks relate to each other? Machine learning is a subset of AI.

Deep Learning 64

Deep Learning Machine Learning Unstructured Data Modeling 64

IBM Big Data Hub

AUGUST 28, 2023

Text mining —also called text data mining—is an advanced discipline within data science that uses natural language processing (NLP) , artificial intelligence (AI) and machine learning models, and data mining techniques to derive pertinent qualitative information from unstructured text data.

Data mining 60

Data mining Machine Learning Deep Learning Marketing 60

datapine

AUGUST 14, 2019

Data science tools are used for drilling down into complex data by extracting, processing, and analyzing structured or unstructured data to effectively generate useful information while combining computer science, statistics, predictive analytics, and deep learning. Source: RStudio. Source: mathworks.com.

Data Science 214

Data Science Statistics Business Intelligence Visualization 214

bridgei2i

OCTOBER 7, 2020

Importance of capturing market data for optimized pricing models. Underwriting essentially means evaluating the risk proposition of an insurance cover and determining how much an insurer should charge for the same by studying historical data and statistical models. if all the information is provided by client or not).

Insurance 52

Insurance Sales Strategy Digital Transformation 52

Domino Data Lab

AUGUST 9, 2021

For a model-driven enterprise, having access to the appropriate tools can mean the difference between operating at a loss with a string of late projects lingering ahead of you or exceeding productivity and profitability forecasts. What Are Modeling Tools? Importance of Modeling Tools. Types of Modeling Tools.

Modeling 111

Modeling Deep Learning Machine Learning Statistics 111

Domino Data Lab

AUGUST 10, 2021

Models are the central output of data science, and they have tremendous power to transform companies, industries, and society. At the center of every machine learning or artificial intelligence application is the ML/AI model that is built with data, algorithms and code. What Is Modeling?

Modeling 64

Modeling IT Risk Management Deep Learning 64

IBM Big Data Hub

AUGUST 1, 2023

True to their name, generative AI models generate text, images, code , or other responses based on a user’s prompt. But what makes the generative functionality of these models—and, ultimately, their benefits to the organization—possible? That’s where the foundation model enters the picture.

Modeling 62

Modeling Cost-Benefit Data Lake Machine Learning 62

DataKitchen

APRIL 13, 2021

We have also included vendors for the specific use cases of ModelOps, MLOps, DataGovOps and DataSecOps which apply DataOps principles to machine learning, AI, data governance, and data security operations. . Dagster / ElementL — A data orchestrator for machine learning, analytics, and ETL. .

Testing 307

Testing Machine Learning Consulting Data Quality 307

IBM Big Data Hub

JANUARY 10, 2024

Data: AI systems learn and make decisions based on data, and they require large quantities of data to train effectively, especially in the case of machine learning (ML) models. For optimal performance, AI models should receive data from a diverse datasets (e.g.,

Unstructured Data 85

Unstructured Data Deep Learning Cost-Benefit Modeling 85

Ontotext

MAY 22, 2023

What is the future of knowledge graphs in the era of ChatGPT and Large Language Models? To start with, Large Language Models (LLM) will not replace databases. They are good for compressing information, but one cannot retrieve from such a model the same information that it got trained on. That’s something that LLMs cannot do.

Management 59

Management Unstructured Data Metadata Cost-Benefit 59

Sisense

MAY 26, 2020

In this post, we’ll discuss these challenges in detail and include some tips and tricks to help you handle text data more easily. Unstructured data and Big Data. Most common challenges we face in NLP are around unstructured data and Big Data. is “big” and highly unstructured.

Unstructured Data 98

Unstructured Data Big Data Testing Machine Learning 98

CIO Business Intelligence

MARCH 9, 2022

As a result, users can easily find what they need, and organizations avoid the operational and cost burdens of storing unneeded or duplicate data copies. Newer data lakes are highly scalable and can ingest structured and semi-structured data along with unstructured data like text, images, video, and audio.

Data Lake 119

Data Lake Unstructured Data Data Warehouse Data Quality 119

Ontotext

FEBRUARY 26, 2024

From a technological perspective, RED combines a sophisticated knowledge graph with large language models (LLM) for improved natural language processing (NLP), data integration, search and information discovery, built on top of the metaphactory platform. Using machine learning, RED indicates the impact of events on stock prices.

Data-driven 59

Data-driven Risk Modeling Risk Management 59

Domino Data Lab

JULY 2, 2019

Paco Nathan ‘s latest article covers program synthesis, AutoPandas, model-driven data queries, and more. In other words, using metadata about data science work to generate code. Doesn’t this seem like a worthy goal for machine learning—to make the machines learn to work more effectively?

Metadata 105

Metadata Machine Learning Data Science Data-driven 105

DataRobot

NOVEMBER 9, 2021

Signal classification models are typically built using time series principles; traditionally used features that include central, windowed, lag, and lead statistics can do the job but sometimes there might be scenarios where we want to eke out more performance out of the data. Image courtesy towardsAI.

Visualization 52

Visualization Unstructured Data Statistics Metrics 52

IBM Big Data Hub

MAY 17, 2023

The services are activated through access management for data collection, analysis and event monitoring in existing drones which are managed by clients and businesses. The flexibility of DaaS in offering a multiplicity of data collection services for different industry use cases makes it unique.

Data Collection 86

Data Collection Visualization Data-driven Unstructured Data 86

datapine

NOVEMBER 19, 2019

An important part of artificial intelligence comprises machine learning, and more specifically deep learning – that trend promises more powerful and fast machine learning. 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

Smart Data Collective

MARCH 16, 2020

Deep learning is likely to play an essential role in keeping costs in check. Deep Learning is Necessary to Create a Sustainable Medicare for All System. He should elaborate more on the benefits of big data and deep learning. They argued that machine learning could make healthcare much more efficient.

Deep Learning 79

Deep Learning Unstructured Data Cost-Benefit Big Data 79