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

It’s a role that requires experience with natural language processing , coding languages, statistical models, and large language and generative AI models. 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

Smart Data Collective

MAY 8, 2022

From the statistics shown, this means that both AI and big data have the potential to affect how we work in the workplace. Above all, there needs to be a set methodology for data mining, collection, and structure within the organization before data is run through a deep learning algorithm or machine learning. Innovations.

Big Data 122

Big Data Data Analytics Analytics Statistics 122

CIO Business Intelligence

OCTOBER 4, 2023

Some certifications in project management , governance, and architecture also attract big bonuses, with CGEIT (Certified in the Governance of Enterprise IT) pulling in a 14% pay premium, up 27% over the last six months, and TOGAF 9 Certified (The Open Group’s Enterprise Architecture Framework certification) attracting a 12%premium, up 9%.

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.” Generative AI uses advanced machine learning algorithms and techniques to analyze patterns and build statistical models.

Enterprise 118

Enterprise Testing Modeling Data-driven 118

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. Conclusion.

Risk 52

Risk Modeling Metrics Business Objectives 52

Domino Data Lab

AUGUST 10, 2021

Some popular tool libraries and frameworks are: Scikit-Learn: used for machine learning and statistical modeling techniques including classification, regression, clustering and dimensionality reduction and predictive data analysis. PyTorch: used for deep learning models, like natural language processing and computer vision.

Modeling 64

Modeling IT Deep Learning Risk Management 64