O'Reilly on Data

DECEMBER 12, 2019

Security vulnerabilities : adversarial actors can compromise the confidentiality, integrity, or availability of an ML model or the data associated with the model, creating a host of undesirable outcomes. The study of security in ML is a growing field—and a growing problem, as we documented in a recent Future of Privacy Forum report. [8].

Machine Learning 191

Machine Learning Modeling Testing Risk Management 191

Cloudera

FEBRUARY 16, 2022

Each AMP consists of a working prototype for a ML use case together with a research report. Each report provides a detailed introduction to the ML algorithm behind each AMP; this includes its applicability to problem families together with examples for usage. Machine Learning Model Interpretability.

Machine Learning 81

Machine Learning Modeling Metadata Recreation/Entertainment 81

Domino Data Lab

OCTOBER 7, 2020

The accuracy of any predictive model approaches 100%. Property 4: The accuracy of any predictive model approaches 100%. This means models can always be found that predict group characteristic with high accuracy. There should be no model to accurately predict even and odd rows with random data.

Statistics 145

Statistics Testing Predictive Modeling Modeling 145

IBM Big Data Hub

FEBRUARY 14, 2024

AI platforms can use machine learning and deep learning to spot suspicious or anomalous transactions. Banks and other lenders can use ML classification algorithms and predictive models to suggest loan decisions.

Cost-Benefit 85

Cost-Benefit Insurance Unstructured Data Machine Learning 85

Smarten

APRIL 19, 2024

Assisted Predictive Modeling and Auto Insights to create predictive models using self-guiding UI wizard and auto-recommendations The Future of AI in Analytics The C=suite executive survey revealed that 93% felt that data strategy is critical to getting value from generative AI, but a full 57% had made no changes to their data.

Analytics 57

Analytics Predictive Modeling KPI Machine Learning 57

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 167

Analytics Machine Learning Data Science Deep Learning 167

CIO Business Intelligence

MARCH 22, 2023

The new class often uses advanced techniques such as deep learning, natural language processing, and computer vision to analyze and extract insights from the data. Up to 44% of data is reported to be inaccessible in an organization. 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

DataRobot Blog

DECEMBER 15, 2022

While AI-powered forecasting can help retailers implement sales and demand forecasting—this process is very complex, and even highly data-driven companies face key challenges: Scale: Thousands of item combinations make it difficult to manually build predictive models. DataRobot Blueprint—from data to predictions.

Forecasting 81

Forecasting Modeling ROI Machine Learning 81

Domino Data Lab

APRIL 21, 2021

Auxiliary techniques like relying on card holders to report fraudulent transactions have unfortunately proven to be ineffective [1]. deep learning) there is no guaranteed explainability. A modern transaction dataset has hundreds of dimensions, millions of records, and is constantly updated in an online fashion.

Statistics 69

Statistics Machine Learning Modeling Metrics 69

Domino Data Lab

JULY 9, 2019

Diving into examples of building and deploying ML models at The New York Times including the descriptive topic modeling-oriented Readerscope (audience insights engine), a prediction model regarding who was likely to subscribe/cancel their subscription, as well as prescriptive example via recommendations of highly curated editorial content.

Data Science 112

Data Science Machine Learning Advertising Modeling 112

O'Reilly on Data

SEPTEMBER 11, 2019

Spark has emerged as the general-purpose data processing engine of choice; interest in Hadoop is waning, although reports of its death are greatly exaggerated. For example, even though ML and ML-related concepts —a related term, “ML models,” (No. Deep learning,” for example, fell year over year to No. 221) to 2019 (No.

IoT 20

IoT Big Data Data Warehouse Uncertainty 20

O'Reilly on Data

MARCH 24, 2020

Understanding the importance of general automation and democratization of all parts of the DS/ML/AI workflow, it’s important to recognize that we’ve done pretty well at democratizing data collection and gathering, modeling [8] , and data reporting [9] , but what remains stubbornly difficult is the whole process of preparing the data.

Machine Learning 346

Machine Learning Statistics Data Quality Modeling 346