IBM Big Data Hub

SEPTEMBER 12, 2023

The UK government’s Ecosystem of Trust is a potential future border model for frictionless trade, which the UK government committed to pilot testing from October 2022 to March 2023. The models also reduce private sector customs data collection costs by 40%.

Testing 91

Testing Modeling Cost-Benefit Consulting 91

Smart Data Collective

MARCH 3, 2023

Then, you make adjustments based on what’s working within your business model— and what isn’t. It’s important to get an objective look at where there are shortcomings in your business model. That’s where modern data tools come in. Using Data to Find Shortcomings & Opportunities No business model is perfect.

Modeling 84

Modeling Cost-Benefit Software Strategy 84

Cloudera

FEBRUARY 16, 2022

In this example, the Machine Learning (ML) model struggles to differentiate between a chihuahua and a muffin. Will the model correctly determine it is a muffin or get confused and think it is a chihuahua? The extent to which we can predict how the model will classify an image given a change input (e.g. Model Visibility.

Machine Learning 83

Machine Learning Modeling Metadata Recreation/Entertainment 83

Domino Data Lab

AUGUST 1, 2021

In this article we cover explainability for black-box models and show how to use different methods from the Skater framework to provide insights into the inner workings of a simple credit scoring neural network model. The interest in interpretation of machine learning has been rapidly accelerating in the last decade. See Ribeiro et al.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139

CIO Business Intelligence

JULY 11, 2022

To meet the customer demands of a digital-first business model, retailers need to address their critical digital infrastructure and rethink network design and cybersecurity. Retailers can leverage the SASE framework to develop overarching network strategies and address the new types of cyber risks within omnichannel models.

Modeling 98

Modeling IoT Cost-Benefit Management 98

Dataiku

OCTOBER 19, 2021

Between energy diversity, climate challenges, and growth in electricity consumption, energy producers and suppliers must constantly optimize their processes and anticipate demand in order to adjust their offers, a strategy based on massive data collection and the deployment of AI solutions.

Modeling 105

Modeling Data Collection Data Science Optimization 105

Smart Data Collective

JUNE 29, 2021

The algorithms will evaluate all the data available regarding you and interpret it in the context of the big data collected worldwide. The thought of insurance companies toying with your data can feel a little frightening. What does this mean for consumers?

Insurance 129

Insurance Modeling IT Big Data 129

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.

Data-driven 279

Data-driven Machine Learning IoT Deep Learning 279

KDnuggets

JANUARY 30, 2023

The ChatGPT Cheat Sheet • ChatGPT as a Python Programming Assistant • How to Select Rows and Columns in Pandas Using [ ],loc, iloc,at and.iat • 5 Free Data Science Books You Must Read in 2023 • From Data Collection to Model Deployment: 6 Stages of a Data Science Project

Data Collection 124

Data Collection Data Science Modeling 124

O'Reilly on Data

MARCH 24, 2020

In a world focused on buzzword-driven models and algorithms, you’d be forgiven for forgetting about the unreasonable importance of data preparation and quality: your models are only as good as the data you feed them. The model and the data specification become more important than the code.

Machine Learning 346

Machine Learning Statistics Data Quality Modeling 346

Rocket-Powered Data Science

JULY 13, 2023

Beyond the early days of data collection, where data was acquired primarily to measure what had happened (descriptive) or why something is happening (diagnostic), data collection now drives predictive models (forecasting the future) and prescriptive models (optimizing for “a better future”).

Analytics 166

Analytics Machine Learning Data Science Deep Learning 166

CIO Business Intelligence

FEBRUARY 12, 2024

This can help to solve the problem of centralized data collection, which is otherwise an impractical approach to a diverse array of data sources, encompassing vehicles, factories, individuals and the environment, and countless other sensors. What are Large Language Models (LLMs)?

Technology 115

Technology Interactive Visualization Modeling 115

KDnuggets

JANUARY 25, 2023

ChatGPT as a Python Programming Assistant • How to Use Python and Machine Learning to Predict Football Match Winners • 20 Questions (with Answers) to Detect Fake Data Scientists: ChatGPT Edition, Part 1 • From Data Collection to Model Deployment: 6 Stages of a Data Science Project • 5 Free Data Science Books You Must Read in 2023

Machine Learning 97

Machine Learning Data Collection Data Science Modeling 97

Rocket-Powered Data Science

MARCH 19, 2021

Focus on specific data types: e.g., time series, video, audio, images, streaming text (such as social media or online chat channels), network logs, supply chain tracking (e.g., Dynamic sense-making, insights discovery, next-best-action response, and value creation is essential when data is being acquired at an enormous rate.

Internet of Things 259

Internet of Things Analytics IoT Prescriptive Analytics 259

CIO Business Intelligence

APRIL 12, 2024

It can be useful for an array of AI-related tasks, including deep learning research, computer vision, natural language processing (NLP), model development, and model deployment. Torch enables fast and efficient GPU support, focusing on improving flexibility and speed when building complex algorithms.

Deep Learning 125

Deep Learning IT Machine Learning IoT 125

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. One type of implementation of a content strategy that is specific to data collections are data catalogs. Data catalogs are very useful and important.

Strategy 266

Strategy Machine Learning Metadata Knowledge Discovery 266

CIO Business Intelligence

APRIL 28, 2024

communication reliability, which supports minute-level data collection and second-level control for low-voltage transparency. By building the enterprise-level unified data foundation, unified AI model factory, and unified IoT platform, State Grid Shaanxi can accumulate valuable know-how assets. HPLC can deliver 99.9%

Interactive 79

Interactive Uncertainty Data Collection IoT 79

O'Reilly on Data

JULY 28, 2020

You must detect when the model has become stale, and retrain it as necessary. The Marketing team built the first model, but because it was from marketing, the model optimized for CTR and lead conversion. Nonetheless, building a superior feature pipeline or model architecture will always be worthwhile.

Marketing 362

Marketing Experimentation Metrics Testing 362

Rocket-Powered Data Science

MARCH 20, 2023

We live in a data-rich, insights-rich, and content-rich world. Data collections are the ones and zeroes that encode the actionable insights (patterns, trends, relationships) that we seek to extract from our data through machine learning and data science.

Data Warehouse 324

Data Warehouse Metadata Digital Transformation Unstructured Data 324

Smart Data Collective

MAY 16, 2022

Data management systems provide a systematic approach to information storage and retrieval and help in streamlining the process of data collection, analysis, reporting, and dissemination. It also helps in providing visibility to data and thus enables the users to make informed decisions.

Management 128

Management Data mining Data Warehouse Data Collection 128

Insight

MARCH 12, 2020

The AIgent was built with BERT, Google’s state-of-the-art language model. In this article, I will discuss the construction of the AIgent, from data collection to model assembly. Data Collection The AIgent leverages book synopses and book metadata. Instead, I built the AIgent. features) and metadata (i.e.

Modeling 59

Modeling Metadata Publishing Sales 59

IBM Big Data Hub

MARCH 27, 2024

This article explores an innovative way to streamline the estimation of Scope 3 GHG emissions leveraging AI and Large Language Models (LLMs) to help categorize financial transaction data to align with spend-based emissions factors. Figure 1 illustrates the framework for Scope 3 emission estimation employing a large language model.

Experimentation 80

Experimentation Modeling Deep Learning Data-driven 80

Cloudera

MAY 9, 2019

The report created a readiness model with five dimensions and various metrics under each dimension. The five dimensions of the readiness model are –. It addresses the key data management challenges with streaming and IoT data for all types of enterprises. Each metric is associated with one or more questions.

IoT 53

IoT Modeling Metrics Measurement 53

CIO Business Intelligence

FEBRUARY 9, 2024

Like many other professional sports leagues, the NFL has been at the leading edge of data-driven transformation for years. By using all the data at its disposal, Digital Athlete can reconstruct the conditions of how and when an injury occurred and run simulations of any play using different sets of players.

Recreation/Entertainment 133

Recreation/Entertainment Data-driven Risk Digital Transformation 133

CIO Business Intelligence

JULY 3, 2023

Data exists in ever larger silos, but real knowledge still resides in employees. But the rise of large language models (LLMs) is starting to make true knowledge management (KM) a reality. These models can extract meaning from digital data at scale and speed beyond the capabilities of human analysts.

Management 105

Management Experimentation Modeling Enterprise 105

O'Reilly on Data

NOVEMBER 13, 2019

While Jonas applauds such inquiry and thinking deeply about the social ramifications of AI research, he is concerned the questions might be reinventing the wheel: “The data collection itself often has serious ramifications that we’ve all been wrestling with for 15 years.

Machine Learning 184

Machine Learning Data Collection Modeling IT 184

IBM Big Data Hub

DECEMBER 16, 2022

Over the last week, millions of people around the world have interacted with OpenAI’s ChatGPT, which represents a significant advance for generative artificial intelligence (AI) and the foundation models that underpin many of these use cases. How can we ensure that these models are being used responsibly?

Uncertainty 116

Uncertainty Metadata Modeling Data Collection 116

Smart Data Collective

MAY 29, 2023

Overcoming representation bias necessitates comprehensive data collection efforts that cover a wide range of languages and dialects, ensuring equal representation and inclusivity. Labeling Bias: Impact on Model Performance The presence of labeling bias in AI translation systems will significantly impact the model’s performance.

Metrics 68

Metrics Measurement Data Collection Strategy 68

Cloudera

APRIL 9, 2021

The first blog introduced a mock vehicle manufacturing company, The Electric Car Company (ECC) and focused on Data Collection. The second blog dealt with creating and managing Data Enrichment pipelines. The third video in the series highlighted Reporting and Data Visualization. Data Collection – streaming data.

Machine Learning 98

Machine Learning Forecasting Manufacturing Predictive Analytics 98

Smart Data Collective

MAY 29, 2023

With these changes comes the challenge of understanding how to gather, manage, and make sense of the data collected in various markets. With the introduction and use of machine learning, AI tech is enabling greater efficiencies with respect to data and the insights embedded in the information.

Data Collection 77

Data Collection Big Data Statistics Software 77

O'Reilly on Data

DECEMBER 10, 2019

We can collect many examples of what we want the program to do and what not to do (examples of correct and incorrect behavior), label them appropriately, and train a model to perform correctly on new inputs. Nor are building data pipelines and deploying ML systems well understood. Instead, we can program by example.

Software 261

Software Machine Learning Risk Data-driven 261

CIO Business Intelligence

NOVEMBER 1, 2023

There’s indeed a lot of hype around the latest wave of large language models (LLM) and associated tools, yet beneath the noise, there’s a whisper about how the technology will one day become indispensable. One common shortcoming of the basic setup of predictive maintenance is that rare events are underrepresented in the training data.

Enterprise 125

Enterprise Digital Transformation Modeling Interactive 125

Occam's Razor

AUGUST 13, 2012

Yehoshua I've covered this topic in detail in this blog post: Multi-Channel Attribution: Definitions, Models and a Reality Check. I explain three different models (Online to Store, Across Multiple Devices, Across Digital Channels) and for each I've highlighted: 1. What's possible to measure.

Modeling 124

Modeling Metrics Data Quality Data-driven 124

DataRobot

AUGUST 25, 2021

In a recent blog, we talked about how, at DataRobot , we organize trust in an AI system into three main categories: trust in the performance in your AI/machine learning model , trust in the operations of your AI system, and trust in the ethics of your modelling workflow, both to design the AI system and to integrate it with your business process.

Metrics 98

Metrics Measurement Machine Learning Data Collection 98

O'Reilly on Data

MAY 14, 2020

Experimentation: It’s just not possible to create a product by building, evaluating, and deploying a single model. In reality, many candidate models (frequently hundreds or even thousands) are created during the development process. Modelling: The model is often misconstrued as the most important component of an AI product.

Management 363

Management Experimentation B2B Machine Learning 363

AWS Big Data

MARCH 26, 2024

In this post, we discuss how you can use purpose-built AWS services to create an end-to-end data strategy for C360 to unify and govern customer data that address these challenges. We recommend building your data strategy around five pillars of C360, as shown in the following figure.

Data Strategy 66

Data Strategy Strategy Data Warehouse Prescriptive Analytics 66

CIO Business Intelligence

OCTOBER 26, 2023

Business intelligence (BI) analysts transform data into insights that drive business value. Business intelligence analyst job requirements BI analysts typically handle analysis and data modeling design using data collected in a centralized data warehouse or multiple databases throughout the organization.

Business Intelligence 127

Business Intelligence Data-driven Statistics Data Warehouse 127