CIO Business Intelligence

JUNE 13, 2025

Queue Hub, begun in earnest in July 2023, was created using machine learning (ML) algorithms, open-source object detection algorithms, and custom AI models. The homegrown platform, now fully operational, runs on AWS and is enhanced continuously, Kundu says. “It

Modeling

DataFloq

MAY 25, 2025

The problem of data silos, where a customer's data is stored across several dis connected systems, hinder the building of a unified view of the customer. These technologies can analyze data to process and provide important features at an exceptional pace. Customers expect to receive instant responses from businesses.

Machine Learning

DataFloq

JUNE 24, 2025

By leveraging machine learning algorithms, businesses can predict customer needs with remarkable accuracy, tailoring interactions to create a more personalized journey that resonates on an emotional level. Moreover, embracing the future of CX means prioritizing transparency and ethical considerations in AI deployment.

Predictive Analytics

AWS Big Data

NOVEMBER 15, 2024

Managed Prometheus allows for real-time high-volume data collection, which scales the ingestion, storage, and querying of operational metrics as workloads increase or decrease. She manages Big Data Operations which includes managing petabyte-scale data and complex workloads processing in cloud.

Big Data

DataFloq

JULY 8, 2025

Webinars stand out among these options for their comprehensive AI integration potential, offering unique advantages in data collection, real-time optimization, and relationship building that complement other digital initiatives.

Interactive

DataFloq

MAY 27, 2025

Teams are able to receive up-to-date data and dashboards in real time and take quick actions based on market changes and customer behavior. DataOps makes all processes easier, starting from data collection to decision-making. Enhanced Data Quality and Governance Poor data is one of the biggest hurdles for activation processes.

Data Quality

CIO Business Intelligence

FEBRUARY 20, 2025

The data retention issue is a big challenge because internally collected data drives many AI initiatives, Klingbeil says. With updated data collection capabilities, companies could find a treasure trove of data that their AI projects could feed on. of their IT budgets on tech debt at that time.

Insurance

CIO Business Intelligence

JANUARY 9, 2025

Before LLMs and diffusion models, organizations had to invest a significant amount of time, effort, and resources into developing custom machine-learning models to solve difficult problems. Companies can enrich these versatile tools with their own data using the RAG (retrieval-augmented generation) architecture.

Software

CIO Business Intelligence

DECEMBER 31, 2024

If followed right, more data can be used in the apps and AI solutions that are built as new data comes in, says Francesco Marzoni, chief data and analytics officer at Ingka, the holding company that runs most of Ikeas department stores. By analyzing this data, their AI competence increases, he says.

Machine Learning

KDnuggets

JULY 31, 2025

They also have a massive proxy network (over 170 million residential IPs) and a handy AI assistant called OxyCopilot that helps automate data collection without any code. They now offer APIs that take care of the full data collection process, so you don’t have to worry about blocks or bans.

Machine Learning

AWS Big Data

JULY 10, 2025

Project managers in the data business were independently designing and building data infrastructure, resulting in duplication of components for data collection, processing, and storage.

Manufacturing

Smart Data Collective

JUNE 22, 2025

Alexandra Bohigian 15 Min Read AI-Generated Image from Google Labs SHARE Since we took over Smart Data Collective, we’ve made it a priority to focus on how artificial intelligence influences the practical side of data mining. The bottom line? Followers Like 33.7k Followers Like 33.7k

Big Data

CIO Business Intelligence

JULY 15, 2025

Recently, SAP has released various applications that generate high-quality business data, and implementing trustworthy AI based on this is a key strategy,” said Irfan Khan, president and chief product officer of SAP Data and Analytics, at a press conference held in Seoul on July 15.

Management

Smart Data Collective

JUNE 26, 2025

SmartData Collective > Big Data > Stopping Lateral Movement in a Data-Heavy, Edge-First World Big Data Exclusive Stopping Lateral Movement in a Data-Heavy, Edge-First World Cybersecurity challenges are growing rapidly as big data and AI reshape the digital threat environment. Followers Like 33.7k

Big Data

CIO Business Intelligence

JANUARY 6, 2025

AI and Machine Learning will drive innovation across the government, healthcare, and banking/financial services sectors, strongly focusing on generative AI and ethical regulation. Data sovereignty and local cloud infrastructure will remain priorities, supported by national cloud strategies, particularly in the GCC.

IoT

DataFloq

JULY 22, 2025

Personalized Recommendations : Leveraging AI and machine learning, it can analyse user preferences, past behaviour, and travel history to offer personalized suggestions. Data Collection and Insights : These chatbots collect valuable data on customer preferences, behaviour, and feedback.

Cost-Benefit

CIO Business Intelligence

JUNE 13, 2025

Generative AI and embedded machine learning capabilities now offer substantial value in everyday business scenarios. The real-time data collected from IoT sensors is no longer a secondary input; it has become vital for enabling smarter, faster and more resilient operations.

Technology

CIO Business Intelligence

MARCH 14, 2025

Ive spent more than 25 years working with machine learning and automation technology, and agentic AI is clearly a difficult problem to solve. Document verification, for instance, might seem straightforward, but it involves multiple steps, including image capture and data collection, behind the scenes.

Technology

CIO Business Intelligence

APRIL 23, 2025

Modern AI models, particularly large language models, frequently require real-time data processing capabilities. The machine learning models would target and solve for one use case, but Gen AI has the capability to learn and address multiple use cases at scale.

Insurance

CIO Business Intelligence

JUNE 23, 2025

This approach supports real-time data collection and analysis, ultimately enhancing decision-making. As AI and machine learning technologies mature, containerized MES/MOM systems will enable predictive maintenance, quality control, and supply chain optimization.

Manufacturing

CIO Business Intelligence

MAY 8, 2025

But that doesnt mask the fact that AI models rely on large amounts of data. The patterns and interdependencies that machine learning (ML) algorithms identify and apply form the basis of their use case. Maintain detailed and transparent records of data sources, collection methods, and preprocessing techniques.

Modeling

O'Reilly on Data

DECEMBER 19, 2018

We need to do more than automate model building with autoML; we need to automate tasks at every stage of the data pipeline. In a previous post , we talked about applications of machine learning (ML) to software development, which included a tour through sample tools in data science and for managing data infrastructure.

Machine Learning

O'Reilly on Data

NOVEMBER 13, 2018

As the data community begins to deploy more machine learning (ML) models, I wanted to review some important considerations. We recently conducted a survey which garnered more than 11,000 respondents—our main goal was to ascertain how enterprises were using machine learning. Privacy and security.

Machine Learning

Jet Global

MARCH 28, 2025

They make use of some of the robust machine learning and artificial intelligence algorithms to help flexible modelling, predictive analytics, seamless integrations, etc. However, these tools are more of data aggregation and data collection solutions than effective planning aids.

Enterprise

Analytics Vidhya

JULY 9, 2022

This article was published as a part of the Data Science Blogathon. Introduction In order to build machine learning models that are highly generalizable to a wide range of test conditions, training models with high-quality data is essential.

Machine Learning

Analytics Vidhya

MARCH 18, 2023

Introduction The advent of the internet and the potential for mass quantitative and qualitative data collection altered the desire for and potential for measuring processes other than those in human resources.

Management

Smart Data Collective

NOVEMBER 5, 2020

Here at Smart Data Collective, we have talked about major changes that machine learning has created in the financial industry. The evolution of smart cards is one of the newest ways that machine learning and AI are impacting the future of finance. How Machine Learning is Changing the Future of Smart Cards.

Machine Learning

O'Reilly on Data

MARCH 24, 2020

Beyond the autonomous driving example described, the “garbage in” side of the equation can take many forms—for example, incorrectly entered data, poorly packaged data, and data collected incorrectly, more of which we’ll address below. The model and the data specification become more important than the code.

Machine Learning

Rocket-Powered Data Science

JULY 6, 2021

Specifically, in the modern era of massive data collections and exploding content repositories, we can no longer simply rely on keyword searches to be sufficient. Labeling, indexing, ease of discovery, and ease of access are essential if end-users are to find and benefit from the collection.

Strategy

O'Reilly on Data

MARCH 31, 2020

If you’re already a software product manager (PM), you have a head start on becoming a PM for artificial intelligence (AI) or machine learning (ML). AI products are automated systems that collect and learn from data to make user-facing decisions. Machine learning adds uncertainty.

Management

KDnuggets

NOVEMBER 4, 2021

Toloka is a crowdsourced data labeling platform that handles data collection and annotation projects for machine learning at any scale. In this Nov 11 Live Demo, Learn how to get reliable training data for machine learning.

Machine Learning

Domino Data Lab

MAY 15, 2019

Machine Learning Projects are Hard: Shifting from a Deterministic Process to a Probabilistic One. Over the years, I have listened to data scientists and machine learning (ML) researchers relay various pain points and challenges that impede their work. Product Management for Machine Learning.

Machine Learning

Smart Data Collective

FEBRUARY 28, 2023

Here at Smart Data Collective, we have blogged extensively about the changes brought on by AI technology. Machine learning technology has already had a huge impact on our lives in many ways. There are numerous ways that machine learning technology is changing the financial industry. What is risk parity?

Machine Learning

O'Reilly on Data

DECEMBER 10, 2019

Roughly a year ago, we wrote “ What machine learning means for software development.” Karpathy suggests something radically different: with machine learning, we can stop thinking of programming as writing a step of instructions in a programming language like C or Java or Python. Instead, we can program by example.

Software

CIO Business Intelligence

OCTOBER 4, 2022

From delightful consumer experiences to attacking fuel costs and carbon emissions in the global supply chain, real-time data and machine learning (ML) work together to power apps that change industries. more machine learning use casesacross the company. By Bryan Kirschner, Vice President, Strategy at DataStax.

Machine Learning

Smart Data Collective

APRIL 10, 2020

One study found that 53% of marketers plan to use machine learning in some capacity. At Smart Data Collective, we have discussed many of the ways that AI and machine learning have changed the face of performance marketing. Machine learning is changing the way that companies position their brand image.

Machine Learning

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.

Machine Learning