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 81

Machine Learning Modeling Metadata Recreation/Entertainment 81

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

Occam's Razor

FEBRUARY 9, 2015

How is competitive intelligence data collected? Competitive intelligence data will never match your site's analytics tool. Traffic Trends Key Metrics Analysis. Onsite Behavior Key Metrics Analysis. Content Consumption Competitive Analysis. + #OMG Mobile, Where's Mobile Data! CI data collection.

Metrics 155

Metrics Reporting IT Advertising 155

Occam's Razor

JULY 18, 2011

The only requirement is that your mental model (and indeed, company culture) should be solidly rooted in permission marketing. You just have to have the right mental model (see Seth Godin above) and you have to… wait for it… wait for it… measure everything you do! Just to ensure you are executing against your right mental model.

Metrics 137

Metrics Marketing Measurement Cost-Benefit 137

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

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 279

Machine Learning Data-driven IoT Deep Learning 279

Occam's Razor

SEPTEMBER 15, 2014

They will need two different implementations, it is quite likely that you will end up with two sets of metrics (more people focused for mobile apps, more visit focused for sites). In this post we will look mobile sites first, both data collection and analysis, and then mobile applications. Media-Mix Modeling/Experimentation.

Metrics 141

Metrics Reporting Analytics Marketing 141

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 332

Management Machine Learning Experimentation Metrics 332

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

Cloudera

MAY 9, 2019

Even if they complete it, they lack the ability to identify and correlate the success metrics with key business goals. The report created a readiness model with five dimensions and various metrics under each dimension. Each metric is associated with one or more questions. The five dimensions of the readiness model are –.

IoT 52

IoT Modeling Metrics Measurement 52

DataRobot

AUGUST 16, 2021

But as the technology’s popularity grows, a number of concerning examples have emerged of AI models operating with algorithmic bias. Algorithmic bias can appear in both supervised and unsupervised AI models. Deciding what bias metric is most relevant requires a contextual interpretation of a use case.

Metrics 98

Metrics Modeling Machine Learning Data Collection 98

Rocket-Powered Data Science

JULY 21, 2023

Here are four specific metrics from the report, highlighting the potentially huge enterprise system benefits coming from implementing Splunk’s observability and monitoring products and services: Four times as many leaders who implement observability strategies resolve unplanned downtime in just minutes, not hours or days.

Machine Learning 189

Machine Learning Metrics Reporting Internet of Things 189

Smart Data Collective

DECEMBER 17, 2021

The process of Marketing Analytics consists of data collection, data analysis, and action plan development. Understanding your marketing data to make more informed and successful marketing strategy decisions is a systematic process. Types of Data Used in Marketing Analytics. Preparing the Data for Analysis.

Marketing Analytics 94

Marketing Analytics Marketing Data-driven Analytics 94

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. That means: All of these metrics are off.

Modeling 124

Modeling Metrics Data Quality Data-driven 124

O'Reilly on Data

NOVEMBER 13, 2018

Considerations for a world where ML models are becoming mission critical. In this post, I share slides and notes from a keynote I gave at the Strata Data Conference in New York last September. As the data community begins to deploy more machine learning (ML) models, I wanted to review some important considerations.

Machine Learning 220

Machine Learning Risk Management Statistics 220

AWS Big Data

MARCH 26, 2024

The following figure shows some of the metrics derived from the study. 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. Organizations using C360 achieved 43.9% reduction in sales cycle duration, 22.8%

Data Strategy 67

Data Strategy Strategy Data Warehouse Prescriptive Analytics 67

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

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

CIO Business Intelligence

APRIL 29, 2022

In this new era the role of humans in the development process also changes as they morph from being software programmers to becoming ‘data producers’ and ‘data curators’ – tasked with ensuring the quality of the input. Many organisations focus too heavily on fine tuning their computational models in their pursuit of ‘quick-wins.’

Data Governance 145

Data Governance IT Risk Data Lake 145

CIO Business Intelligence

APRIL 27, 2023

At this time of dynamic business and market changes, uncertainty, and quickly evolving consumption models for IT infrastructure, every IT executive understands the benefits and necessity of network agility. We’ve seen how it can gather and organize telemetry data collected from all parts of a company’s network.

Management 93

Management Internet of Things Cost-Benefit Uncertainty 93

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 82

Data Science Data Analytics Prescriptive Analytics Analytics 82

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 131

Business Analytics Prescriptive Analytics Data mining Diagnostic Analytics 131

IBM Big Data Hub

FEBRUARY 23, 2024

DL models can improve over time through further training and exposure to more data. When a user sends a message, the system uses NLP to parse and understand the input, often by using DL models to grasp the nuances and intent. Marketing and sales: Conversational AI has become an invaluable tool for data collection.

Enterprise 103

Enterprise Interactive Data-driven Key Performance Indicator 103

The Unofficial Google Data Science Blog

JULY 18, 2023

Once we’ve answered that, we will then define and use metrics to understand the quality of human-labeled data, along with a measurement framework that we call Cross-replication Reliability or xRR. Last, we’ll provide a case study of how xRR can be used to measure improvements in a data-labeling platform.

Measurement 52

Measurement Metrics Uncertainty Slice and Dice 52

Ontotext

AUGUST 12, 2021

The hospital (and many other Healthcare institutions like it) keeps the data in various systems where each serves the specific needs of a different department and there is no unified access or identification of individuals between databases. Custom metrics are no different.

Metrics 98

Metrics Visualization Reporting Data Collection 98

Jet Global

AUGUST 11, 2020

Reassessing the Current Business Model. There are many benefits that can come with digital transformation, but the implementation of these changes needs to offer a clear intent and benefit to the company’s business model. In some cases, this can go as far as ushering in a new business model altogether.

Digital Transformation 90

Digital Transformation Finance Machine Learning Data Collection 90

Smart Data Collective

SEPTEMBER 4, 2020

Google has shown how to use big data effectively for decision-making , but many other companies don’t understand the principles to follow. Far too many businesses fail to develop a sensible data strategy, so their ROI from their data collection methodologies is often subpar. Guide to Creating a Big Data Strategy.

Data-driven 87

Data-driven Big Data Metrics Strategy 87

CIO Business Intelligence

MARCH 21, 2022

A data scientist’s chief responsibility is data analysis, which begins with data collection and ends with business decisions based on analytic results. The data that data scientists analyze draws from many sources, including structured, unstructured, or semi-structured data.

Unstructured Data 135

Unstructured Data Data Analytics Analytics Structured Data 135

DataRobot

OCTOBER 20, 2021

Data literacy ensures that diverse perspectives are baked into AI governance and leads to the production of AI systems that achieve better and more consistent outcomes. It is a key component of developing responsible AI, and promotes trust not only in AI concepts but also in individual AI models. What Is Data Literacy?

Machine Learning 52

Machine Learning Metrics Risk Deep Learning 52

CIO Business Intelligence

MAY 16, 2023

AI Opportunities Generative AI is the basis for sophisticated AI models such as ChatGPT and Dall-E. Big Data collection at scale is increasing across industries, presenting opportunities for companies to develop AI models and leverage insights from that data. This helps to ensure accuracy and neutrality.

Advertising 92

Advertising Recreation/Entertainment Experimentation Enterprise 92

CIO Business Intelligence

SEPTEMBER 8, 2022

Most organizations understand the profound impact that data is having on modern business. In Foundry’s 2022 Data & Analytics Study , 88% of IT decision-makers agree that data collection and analysis have the potential to fundamentally change their business models over the next three years.

Cost-Benefit 115

Cost-Benefit Internet of Things Data-driven Data Lake 115

DataRobot Blog

JULY 27, 2022

In this technical post, we’ll focus on some changes we’ve made to allow custom models to operate as an algorithm on Algorithmia, while still feeding predictions, input, and other metrics back to the DataRobot MLOps platform —a true best of both worlds. Data Science Expertise Meets Scalability. Running the Notebook.

Metrics 52

Metrics Deep Learning Modeling Machine Learning 52

IBM Big Data Hub

FEBRUARY 7, 2024

2 For example, some are turning to software solutions that can more easily capture, manage and report ESG data. Circular economy: When waste is a resource Waste not, want not: the circular economy model, which aims to minimize unnecessary waste and make the most of resources, is booming. trillion in economic benefits by 2030.

Reporting 96

Reporting Cost-Benefit Internet of Things Data-driven 96

CIO Business Intelligence

JUNE 5, 2023

By integrating remote working capabilities, the CIO plays a hand in an organization’s shift to an increasingly remote or hybrid workforce model—a move that can significantly reduce a company’s carbon footprint. This opportunity to collaborate with sustainability in mind extends to the influence CIOs hold over where and how employees work.

Software 103

Software Measurement Visualization Business Objectives 103

Sisense

SEPTEMBER 3, 2020

Data Scientist. Data scientists usually build models for data-driven decisions asking challenging questions that only complex calculations can try to answer and creating new solutions where necessary. While salaries for data analysts are often reasonably high, salaries for data scientists may be higher still.

Statistics 97

Statistics Metrics Visualization Finance 97

CIO Business Intelligence

APRIL 14, 2022

Digitizing operations, experiences, and products will not only save time and money, but also increase speed to insight by breaking down silos and making critical data more accessible. Smarter operations through integrated data and analytics. For example, a client in the oil and gas sector recently equipped their U.S.

Strategy 110

Strategy Digital Transformation IoT Measurement 110

FineReport

MAY 31, 2023

Financial Performance Dashboard The financial performance dashboard provides a comprehensive overview of key metrics related to your balance sheet, shedding light on the efficiency of your capital expenditure. The Dupont analysis model comprises three key components: profit margin, asset turnover, and equity multiplier.

Dashboards 52

Dashboards Key Performance Indicator Metrics Visualization 52