O'Reilly on Data

DECEMBER 12, 2019

In addition to newer innovations, the practice borrows from model risk management, traditional model diagnostics, and software testing. While our analysis of each method may appear technical, we believe that understanding the tools available, and how to use them, is critical for all risk management teams.

Machine Learning 285

Machine Learning Modeling Testing Risk Management 285

CIO Business Intelligence

OCTOBER 13, 2023

The role of algorithm engineer requires knowledge of programming languages, testing and debugging, documentation, and of course algorithm design. Deep learning is a subset of AI , and vital to the development of gen AI tools and resources in the enterprise.

Deep Learning 143

Deep Learning Machine Learning Modeling Software 143

CIO Business Intelligence

OCTOBER 4, 2023

There’s also strong demand for non-certified security skills, with DevSecOps, security architecture and models, security testing, and threat detection/modelling/management attracting the highest pay premiums.

Prescriptive Analytics 135

Prescriptive Analytics IT Machine Learning Deep Learning 135

IBM Big Data Hub

FEBRUARY 13, 2024

Generative AI represents a significant advancement in deep learning and AI development, with some suggesting it’s a move towards developing “ strong AI.” Fraud detection and risk management : Generative AI can quickly scan and summarize large amounts of data to identify patterns or anomalies.

Enterprise 118

Enterprise Testing Modeling Data-driven 118

CIO Business Intelligence

MARCH 13, 2024

L’analisi dei dati attraverso l’apprendimento automatico (machine learning, deep learning, reti neurali) è la tecnologia maggiormente utilizzata dalle grandi imprese che utilizzano l’IA (51,9%). Le reti neurali sono il modello di machine learning più utilizzato oggi. L’IA non è una tecnologia completamente matura.

Machine Learning 105

Machine Learning Deep Learning Big Data Testing 105

DataRobot Blog

MARCH 7, 2023

DataRobot on Azure accelerates the machine learning lifecycle with advanced capabilities for rapid experimentation across new data sources and multiple problem types. This generates reliable business insights and sustains AI-driven value across the enterprise. The DataRobot AI Platform is the next generation of AI.

Data-driven 59

Data-driven Machine Learning Experimentation Data Lake 59

DataRobot Blog

MAY 26, 2022

To start with, SR 11-7 lays out the criticality of model validation in an effective model risk management practice: Model validation is the set of processes and activities intended to verify that models are performing as expected, in line with their design objectives and business uses.

Risk 52

Risk Modeling Metrics Business Objectives 52