IBM Big Data Hub

MARCH 7, 2023

Years’ worth of aggregated data in the NICU could help lead us to a solution. The challenge was gleaning relevant insights from the vast amount of data collected to help identify those infants at risk. We wanted to be able to help them observe and monitor the thousands of data points available to make informed decisions.

Risk 52

Risk Technology Informatics Predictive Modeling 52

Smarten

MAY 31, 2022

The technology research firm, Gartner has predicted that, ‘predictive and prescriptive analytics will attract 40% of net new enterprise investment in the overall business intelligence and analytics market.’ It is meant to identify crucial relationships and opportunities and risks and help the organization to accurately predict: Growth.

Predictive Analytics 67

Predictive Analytics Analytics Predictive Modeling Prescriptive Analytics 67

O'Reilly on Data

MAY 18, 2020

This article answers these questions, based on our combined experience as both a lawyer and a data scientist responding to cybersecurity incidents, crafting legal frameworks to manage the risks of AI, and building sophisticated interpretable models to mitigate risk. All predictive models are wrong at times?—just

Risk 359

Risk Modeling Data Processing Software 359

IBM Big Data Hub

MAY 18, 2023

This report outlines the combination of traditional decision automation tools with machine learning models and other technologies. As Forrester notes in the report, many organizations are eager to harness the power of AI but also must be cautious of risks.

Reporting 91

Reporting Scorecard Predictive Modeling Machine Learning 91

CIO Business Intelligence

JUNE 7, 2022

Monte Carlo simulation: According to Investopedia , “Monte Carlo simulations are used to model the probability of different outcomes in a process that cannot easily be predicted due to the intervention of random variables.” It is frequently used for risk analysis. Data analytics and data science are closely related.

Data Analytics 135

Data Analytics Diagnostic Analytics Management Analytics 135

Business Over Broadway

SEPTEMBER 27, 2018

In our world of Big Data, marketers no longer need to simply rely on their gut instincts to make marketing decisions. Through the application of data science principles, marketing professionals now have a way of making evidence-based decisions to improve their marketing activities. genetic counseling, genetic testing).

Machine Learning 76

Machine Learning Data Integration Marketing Predictive Modeling 76

O'Reilly on Data

DECEMBER 12, 2019

Because all ML models make mistakes, everyone who cares about ML should also care about model debugging. [1] 1] This includes C-suite executives, front-line data scientists, and risk, legal, and compliance personnel. That’s where model debugging comes in. Interpretable ML models and explainable ML.

Machine Learning 191

Machine Learning Modeling Testing Risk Management 191

DataRobot

MAY 25, 2021

At the beginning of the pandemic, DataRobot committed to fighting against COVID-19 from a data science perspective. As part of these efforts, we built accurate predictive models to determine the spread of the disease weeks and months in advance of a surge.

Testing 52

Testing Predictive Modeling Data-driven Reporting 52

Cloudera

MAY 12, 2021

We welcome organizations that have built and deployed use cases for enterprise-scale machine learning and have industrialized AI to automate, secure, and optimize data-driven decision-making and/or applications to enter this category. Read more about last years Data Impact Award winners. HYBRID & MULTI-CLOUD INNOVATION.

Digital Transformation 65

Digital Transformation Machine Learning Optimization Data Lake 65

Smarten

AUGUST 21, 2019

Smart Data Visualization – Smart Data Visualization gives users a helping hand with suggestions and recommendations for the best possible visualization of underlying data to generate meaningful insights from data.

Analytics 51

Analytics Predictive Modeling Visualization Statistics 51

DataRobot Blog

JUNE 23, 2022

Your artificial intelligence (AI) system has given you this “predicted feature” in addition to what you already know about the applicant. Business rules set by the credit committee to control business risk and the loan portfolio also constrain accepting this application and advancing the loan. Case Study.

Predictive Modeling 59

Predictive Modeling Modeling Data Science Measurement 59

CIO Business Intelligence

OCTOBER 13, 2023

Data scientist As companies embrace gen AI, they need data scientists to help drive better insights from customer and business data using analytics and AI. For most companies, AI systems rely on large datasets, which require the expertise of data scientists to navigate.

Deep Learning 143

Deep Learning Machine Learning Modeling Software 143

IBM Big Data Hub

OCTOBER 16, 2023

Marketers use ML for lead generation, data analytics, online searches and search engine optimization (SEO). ML algorithms and data science are how recommendation engines at sites like Amazon, Netflix and StitchFix make recommendations based on a user’s taste, browsing and shopping cart history.

Machine Learning 49

Machine Learning Marketing Forecasting Modeling 49

DataRobot Blog

APRIL 1, 2022

In many cases, many models in the pipeline do not ever make it to production, and among those that do, controlling and managing them in different environments can be difficult. Also, degrading models over time can pose a significant risk to the business. I recently gave a talk on this topic at the AI Summit Silicon Valley.

Machine Learning 59

Machine Learning Cost-Benefit Predictive Modeling Modeling 59

IBM Big Data Hub

AUGUST 28, 2023

One of the best ways to take advantage of social media data is to implement text-mining programs that streamline the process. Text representation In this stage, you’ll assign the data numerical values so it can be processed by machine learning (ML) algorithms, which will create a predictive model from the training inputs.

Data mining 56

Data mining Machine Learning Deep Learning Marketing 56

Cloudera

JULY 18, 2018

To arrive at quality data, organizations are spending significant levels of effort on data integration, visualization, and deployment activities. Additionally, organizations are increasingly restrained due to budgetary constraints and having limited data sciences resources.

Machine Learning 50

Machine Learning Predictive Analytics Analytics Prescriptive Analytics 50

DataRobot Blog

NOVEMBER 9, 2017

I’ve spent the last 2+ years working with business analysts and MBA’s to build predictive models. We started offering a course called Data Science, Machine Learning, and AI for Executives a while back, and it’s been very successful. Improve the way your organization maintains its equipment using predictive maintenance.

Machine Learning 52

Machine Learning Data-driven Software Sales 52

O'Reilly on Data

MARCH 20, 2019

Apply fair and private models, white-hat and forensic model debugging, and common sense to protect machine learning models from malicious actors. Like many others, I’ve known for some time that machine learning models themselves could pose security risks. they can train their own surrogate model.

Modeling 222

Modeling Machine Learning Predictive Modeling Consulting 222

The Unofficial Google Data Science Blog

DECEMBER 4, 2019

by THOMAS OLAVSON Thomas leads a team at Google called "Operations Data Science" that helps Google scale its infrastructure capacity optimally. It provides the occasion for deeper exploration of which inputs that can be influenced and which risks can be proactively managed. Our team does a lot of forecasting.

Forecasting 113

Forecasting Data Science Statistics Uncertainty 113

Insight

FEBRUARY 19, 2020

Using variability in machine learning predictions as a proxy for risk can help studio executives and producers decide whether or not to green light a film project Photo by Kyle Smith on Unsplash Originally posted on Toward Data Science. A single prediction of ROI output from a single model would not be very trustworthy.

Risk 67

Risk ROI Modeling Metrics 67

Domino Data Lab

JANUARY 21, 2021

Producing insights from raw data is a time-consuming process. Predictive modeling efforts rely on dataset profiles , whether consisting of summary statistics or descriptive charts. The Importance of Exploratory Analytics in the Data Science Lifecycle. There is a risk of injecting bias. ref: [link].

Statistics 106

Statistics Unstructured Data Data Science Visualization 106

DataRobot Blog

JANUARY 10, 2023

ML model builders spend a ton of time running multiple experiments in a data science notebook environment before moving the well-tested and robust models from those experiments to a secure, production-grade environment for general consumption. Deep Dive into DataRobot Notebooks. Auto-scale compute.

Experimentation 52

Experimentation Machine Learning Data Science Modeling 52

O'Reilly on Data

SEPTEMBER 19, 2023

While data scientists were no longer handling Hadoop-sized workloads, they were trying to build predictive models on a different kind of “large” dataset: so-called “unstructured data.” “Here’s our risk model. And it was good. For a few years, even.

Machine Learning 247

Machine Learning Testing Modeling Cost-Benefit 247

Domino Data Lab

JULY 9, 2019

Chris Wiggins , Chief Data Scientist at The New York Times, presented “Data Science at the New York Times” at Rev. Wiggins also indicated that data science, data engineering, and data analysis are different groups at The New York Times. Session Summary. Transcript. Feel free to email me.

Data Science 112

Data Science Machine Learning Advertising Modeling 112

IBM Big Data Hub

DECEMBER 20, 2023

ML is a computer science, data science and artificial intelligence (AI) subset that enables systems to learn and improve from data without additional programming interventions. Regression algorithms —predict output values by identifying linear relationships between real or continuous values (e.g.,

Machine Learning 77

Machine Learning Modeling Deep Learning Predictive Modeling 77

CIO Business Intelligence

AUGUST 15, 2023

Whether a project aims to improve suicide prevention using data science or to create new revenue streams by reimagining an organization’s core business, CIO 100 Award winners demonstrate the innovative spirit of today’s IT in the face of rapidly evolving organizational challenges.

IT 98

IT Manufacturing IoT Cost-Benefit 98

Cloudera

NOVEMBER 18, 2020

With this functionality, business units can now leverage big data analytics to develop better and faster insights to help achieve better revenues, higher productivity, and decrease risk. . Data Champions . The pipeline provides its clinicians fast access to real-time patient data and prediction models.

Internet Publishing and Broadcasting 118

Internet Publishing and Broadcasting Data-driven Broadcasting Digital Transformation 118

Cloudera

APRIL 13, 2021

Organizations from across the globe and virtually every industry have used CDP to generate new revenue streams, decrease operational costs, and mitigate risks. Disparate data silos made real-time streaming analytics, data science, and predictive modeling nearly impossible.

Enterprise 73

Enterprise Cost-Benefit Data Warehouse Data Architecture 73

CIO Business Intelligence

MARCH 22, 2023

It is often used to train machine learning models and protect sensitive data in healthcare and finance. Synthetic data can generate large amounts of data quickly and bypass privacy risks. This can result in accuracy loss and undermine the model’s capabilities in the inference stage.

Machine Learning 105

Machine Learning Cost-Benefit Modeling Risk 105

Insight

MARCH 18, 2019

Bias in Machine Learning Algorithms (Bottom Photos Source: ProPublica ; Top Photos Source: Pexels.com) Biases in predictive modeling are a widespread issue Machine learning and AI applications are used across industries, from recommendation engines to self-driving cars and more. Overview of what this project aims to build.

Machine Learning 77

Machine Learning Predictive Modeling Modeling Metrics 77

Alation

APRIL 14, 2022

The vision of big data freed organizations to capture more data sources at lower levels of detail and in vastly greater volumes. For example, data science always consumes “historical” data, and there is no guarantee that the semantics of older datasets are the same, even if their names are unchanged.

Metadata 59

Metadata Data Governance Modeling Data-driven 59

Sisense

SEPTEMBER 25, 2020

Linear regression is a form of supervised learning (or predictive modeling). In supervised learning, the dependent variable is predicted from the combination of independent variables. When a single independent variable is used to predict the value of a dependent variable, it’s called simple linear regression. Clustering.

Statistics 98

Statistics Predictive Modeling Modeling Machine Learning 98

Domino Data Lab

APRIL 21, 2021

Rules-based fraud detection (top) vs. classification decision tree-based detection (bottom): The risk scoring in the former model is calculated using policy-based, manually crafted rules and their corresponding weights. Knowledge and Data Engineering, IEEE Transactions on, 21, 1263-1284. References. [1] Pavía, Ernesto J.

Statistics 69

Statistics Machine Learning Modeling Metrics 69

Cloudera

SEPTEMBER 5, 2018

Having clean, validated, conformed historical data in a single repository is critical for an effective AML program. It provides more relevant matches for KYC screening, enables better predictive algorithms for risk scoring, shortens time spent on investigation, and cuts down on costs to support periodic regulatory reviews.

Machine Learning 40

Machine Learning Risk Cost-Benefit Big Data 40

Rocket-Powered Data Science

OCTOBER 6, 2023

In these applications, the data science involvement includes both the “learning” of the most significant patterns to alert on and the improvement of their models (logic) to minimize false positives and false negatives. Broken models are definitely disruptive to analytics applications and business operations.

Business Analytics 157

Business Analytics Prescriptive Analytics Analytics Diagnostic Analytics 157

O'Reilly on Data

SEPTEMBER 11, 2019

Machine learning, artificial intelligence, data engineering, and architecture are driving the data space. The Strata Data Conferences helped chronicle the birth of big data, as well as the emergence of data science, streaming, and machine learning (ML) as disruptive phenomena.

IoT 20

IoT Big Data Data Warehouse Uncertainty 20

DataRobot

JANUARY 20, 2022

Specialized teams from DataRobot and Snowflake will enable ICSs to mitigate data governance and model bias risk with confidence. Public sector data sharing. ICSs can reduce the time taken to build population health registries and predictive models by up to 90 percent. Grasping the digital opportunity.

Data-driven 145

Data-driven Experimentation Modeling Predictive Modeling 145