Domino Data Lab

AUGUST 6, 2021

Model Risk Management is about reducing bad consequences of decisions caused by trusting incorrect or misused model outputs. Systematically enabling model development and production deployment at scale entails use of an Enterprise MLOps platform, which addresses the full lifecycle including Model Risk Management.

Risk Management 64

Risk Management Risk Modeling Enterprise 64

DataRobot Blog

APRIL 29, 2022

The stakes in managing model risk are at an all-time high, but luckily automated machine learning provides an effective way to reduce these risks. However, after the financial crisis, financial regulators around the world stepped up to the challenge of reigning in model risk across the financial industry.

Risk 111

Risk Modeling IT Risk Management 111

CIO Business Intelligence

OCTOBER 15, 2024

Driven by the development community’s desire for more capabilities and controls when deploying applications, DevOps gained momentum in 2011 in the enterprise with a positive outlook from Gartner and in 2015 when the Scaled Agile Framework (SAFe) incorporated DevOps. It may surprise you, but DevOps has been around for nearly two decades.

Enterprise 143

Enterprise Testing Business Objectives Experimentation 143

AWS Big Data

JULY 16, 2024

Zurich has done testing with Amazon SageMaker and has plans to add this capability in the near future. Austin Rappeport is a Computer Engineer who graduated from the University of Illinois Urbana/Champaign in 2011 with a focus in Computer Security.

Insurance 130

Insurance Management Cost-Benefit Optimization 130

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

Domino Data Lab

APRIL 3, 2019

Also, while surveying the literature two key drivers stood out: Risk management is the thin-edge-of-the-wedge ?for Fun fact: in 2011 Google bought remnants of what had previously been Motorola. data to train and test models poses new challenges: The need for reproducibility in analytics workflows becomes more acute.

Machine Learning 84

Machine Learning Data Governance Metadata Data Science 84

datapine

AUGUST 29, 2022

He founded the project Apache Storm in 2011, which turned to be “one of the world’s most popular stream processors and has been adopted by many of the world’s largest companies, including Yahoo!, Microsoft, Alibaba, Taobao, WebMD, Spotify, Yelp” according to Marz himself. is one of the greatest on the market.

Big Data 263

Big Data Data Analytics Analytics Data mining 263