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 343

Machine Learning Statistics Data Quality Modeling 343

IBM Big Data Hub

MARCH 20, 2024

In the business sphere, both large enterprises and small startups depend on public cloud computing models to provide the flexibility, cost-effectiveness and scalability needed to fuel business growth. In a public cloud computing model, a cloud service provider (CSP) owns and operates vast physical data centers that run client workloads.

Internet of Things 90

Internet of Things Cost-Benefit Data Processing IoT 90

IBM Big Data Hub

SEPTEMBER 28, 2023

An AI and data platform, such as watsonx, can help empower businesses to leverage foundation models and accelerate the pace of generative AI adoption across their organization. Business-targeted, IBM-developed foundation models built from sound data Business leaders charged with adopting generative AI need model flexibility and choice.

Testing 89

Testing Finance Enterprise Modeling 89

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 122

Deep Learning IT Machine Learning IoT 122

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 124

Business Intelligence Data-driven Statistics Data Warehouse 124

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 258

Machine Learning Software Metadata Testing 258

CIO Business Intelligence

AUGUST 25, 2023

Hot Melt Optimization employs a proprietary data collection method using proprietary sensors on the assembly line, which, when combined with Microsoft’s predictive analytics and Azure cloud for manufacturing, enables P&G to produce perfect diapers by reducing loss due to damage during the manufacturing process.

Predictive Analytics 95

Predictive Analytics IoT Manufacturing Analytics 95

Smart Data Collective

APRIL 20, 2022

Companies that utilize data analytics to make the most of their business model will have an easier time succeeding with Amazon. One of the best ways to create a profitable business model with Amazon involves using data analytics to optimize your PPC marketing strategy.

Data Quality 115

Data Quality Analytics Testing Sales 115

CIO Business Intelligence

JULY 5, 2022

Business analytics is the practical application of statistical analysis and technologies on business data to identify and anticipate trends and predict business outcomes. Data analytics is used across disciplines to find trends and solve problems using data mining , data cleansing, data transformation, data modeling, and more.

Business Analytics 120

Business Analytics Prescriptive Analytics Data mining Diagnostic Analytics 120

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

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 67

Interactive Uncertainty Data Collection IoT 67

Smart Data Collective

OCTOBER 30, 2021

Last year, one expert reported that Netflix used big data to grow to become a $100 billion company. This shouldn’t surprise anybody, because big data has been instrumental in their business model since the day the company was launched. But what data does it actually process? Is it only watch history?

Big Data 114

Big Data Data Collection Testing Modeling 114

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

Smart Data Collective

OCTOBER 8, 2023

The Power of Data Analytics: An Overview Data analytics, in its simplest form, is the process of inspecting, cleansing, transforming, and modeling data to unearth useful information, draw conclusions, and support decision-making. This involves data collection , data cleaning, data analysis, and data interpretation.

Data Analytics 94

Data Analytics Analytics Statistics Data Collection 94

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

O'Reilly on Data

OCTOBER 22, 2020

Taking the time to work this out is like building a mathematical model: if you understand what a company truly does, you don’t just get a better understanding of the present, but you can also predict the future. Since I work in the AI space, people sometimes have a preconceived notion that I’ll only talk about data and models.

Consulting 292

Consulting Risk Cost-Benefit Modeling 292

DataKitchen

JANUARY 23, 2023

DataOps Observability includes monitoring and testing the data pipeline, data quality, data testing, and alerting. Data testing is an essential aspect of DataOps Observability; it helps to ensure that data is accurate, complete, and consistent with its specifications, documentation, and end-user requirements.

Testing 130

Testing Data Governance Data Quality Data-driven 130

CIO Business Intelligence

JANUARY 3, 2024

According to Martin Rapos, CEO of AR/VR platform Akular, which converts 3D models into digital twins, the impact of every dollar spent early is an order of magnitude higher than if you spend it five years down the road as a follower. The most effective way to get results quickly is to work with a platform that enables multiple use cases.

Cost-Benefit 116

Cost-Benefit ROI Manufacturing Optimization 116

CIO Business Intelligence

MARCH 30, 2022

Even when customers have tuned their rules for years with fraud analysts who know the space, we come in with machine learning models and beat them,” says Baumhof. But six years ago, when LexisNexis began to invest in machine learning as a replacement for rules-based systems, the company started with a linear regression model.

Machine Learning 142

Machine Learning Testing Risk Data-driven 142

DataKitchen

MARCH 16, 2023

Errors can originate from various sources, including data collection, integration, models, visualization, governance, and security. Organizations will make better-informed, data-driven decisions by minimizing errors and avoiding the negative consequences of relying on inaccurate information. Where do you find errors?

Metrics 130

Metrics Data Analytics Analytics Measurement 130

datapine

JANUARY 6, 2022

Qualitative data, as it is widely open to interpretation, must be “coded” so as to facilitate the grouping and labeling of data into identifiable themes. There are few certainties when it comes to data analysis, but you can be sure that if the research you are engaging in has no numbers involved, it is not quantitative research.

Visualization 246

Visualization Dashboards Cost-Benefit Measurement 246

Rocket-Powered Data Science

JUNE 17, 2022

Here is a list of my top moments, learnings, and musings from this year’s Splunk.conf : Observability for Unified Security with AI (Artificial Intelligence) and Machine Learning on the Splunk platform empowers enterprises to operationalize data for use-case-specific functionality across shared datasets. is here, now!

Machine Learning 238

Machine Learning Recreation/Entertainment Risk Business Objectives 238

CIO Business Intelligence

SEPTEMBER 14, 2023

What is a data engineer? Data engineers design, build, and optimize systems for data collection, storage, access, and analytics at scale. They create data pipelines that convert raw data into formats usable by data scientists, data-centric applications, and other data consumers.

Analytics 128

Analytics Data Science Unstructured Data Data mining 128

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

Rocket-Powered Data Science

JULY 21, 2023

” Model-Assisted Threat Hunts , also known as Splunk M-ATH , is Splunk’s brand name for machine learning-assisted threat hunting and mitigation. testing for hypothesized threats, behaviors, and activities), (2) Baseline (i.e., automation of the first two type of hunts, using AI and machine learning).

Machine Learning 190

Machine Learning Metrics Reporting Internet of Things 190

IBM Big Data Hub

SEPTEMBER 19, 2023

Though you may encounter the terms “data science” and “data analytics” being used interchangeably in conversations or online, they refer to two distinctly different concepts. Meanwhile, data analytics is the act of examining datasets to extract value and find answers to specific questions.

Data Science 78

Data Science Data Analytics Prescriptive Analytics Analytics 78

datapine

JANUARY 31, 2022

The companies that are most successful at marketing in both B2C and B2B are using data and online BI tools to craft hyper-specific campaigns that reach out to targeted prospects with a curated message. Everything is being tested, and then the campaigns that succeed get more money put into them, while the others aren’t repeated.

Business Intelligence 214

Business Intelligence Analytics Cost-Benefit ROI 214

datapine

MAY 2, 2023

Your Chance: Want to test a professional logistics analytics software? 10 Essential Big Data Use Cases in Logistics Now that you’re up to speed on the perks of investing in analytics, let’s look at some practical examples that highlight the growing importance of data in logistics, based on different business scenarios.

Big Data 275

Big Data Cost-Benefit Internet of Things Optimization 275

Cloudera

MARCH 18, 2021

A revised internal models approach (IMA) combined with an overhaul of the standardized approach (SA). Continuous monitoring will be required, and banks will need to conduct back-testing to ensure accuracy. And there will be expansions on the requirements for managing and monitoring both data lineage and data security.

Risk 55

Risk Machine Learning Risk Management Measurement 55

Smart Data Collective

AUGUST 2, 2022

Automotive OEMs and top automotive software companies can work together to build resilient software development processes with sophisticated AI algorithms that allow them to innovate, meet growing customer needs for infotainment systems, and monetize new business models.

Software 127

Software Machine Learning B2B Manufacturing 127

O'Reilly on Data

MARCH 31, 2020

Instead of writing code with hard-coded algorithms and rules that always behave in a predictable manner, ML engineers collect a large number of examples of input and output pairs and use them as training data for their models. The model is produced by code, but it isn’t code; it’s an artifact of the code and the training data.

Management 327

Management Machine Learning Experimentation Metrics 327

DataRobot Blog

DECEMBER 1, 2022

Artificial intelligence (AI) can help improve the response rate on your coupon offers by letting you consider the unique characteristics and wide array of data collected online and offline of each customer and presenting them with the most attractive offers. Training and Testing Different AI Models.

Machine Learning 52

Machine Learning Marketing Modeling Statistics 52

CIO Business Intelligence

MAY 26, 2022

Is there a model that can provide the necessary results? But the tools that data scientists use to create these proofs of concept often don’t translate well into production systems. As a result, it can take more than nine months on average to deploy an AI or ML solution, according to IDC data. “We How can it be built?

Machine Learning 137

Machine Learning Modeling Data-driven Dashboards 137

datapine

SEPTEMBER 1, 2020

Your Chance: Want to test a market research reporting software? An effective modern means of extracting real value from your research results such as brand analysis, market research reports present and arrange data in a way that is digestible and logical in equal measures through professional online reporting software and tools.

Reporting 247

Reporting Marketing KPI Dashboards 247

IBM Big Data Hub

AUGUST 11, 2023

Machine learning engineers take massive datasets and use statistical methods to create algorithms that are trained to find patterns and uncover key insights in data mining projects. These insights can help drive decisions in business, and advance the design and testing of applications. How the models are stored.

Data Science 54

Data Science Machine Learning Cost-Benefit Deep Learning 54

Cloudera

AUGUST 17, 2020

For this step, we used Spark ML in CML ( Cloudera Machine Learning ) to train a Naïve Bayes model to identify what a technical sentence looks like in the context of a case comment. Data Collection Request – A sentence requesting data from the customer. Testing the Results. Extract Technical Sentences.

Testing 86

Testing Machine Learning Interactive Metrics 86

Domino Data Lab

MAY 8, 2019

Paco Nathan’s latest article features several emerging threads adjacent to model interpretability. I’ve been out themespotting and this month’s article features several emerging threads adjacent to the interpretability of machine learning models. Machine learning model interpretability. Introduction. 2018-06-21).

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105