Domino Data Lab

AUGUST 6, 2021

Model Risk Management is about reducing bad consequences of decisions caused by trusting incorrect or misused model outputs. An enterprise starts by using a framework to formalize its processes and procedures, which gets increasingly difficult as data science programs grow. What Is Model Risk?

Risk Management 64

Risk Management Risk Modeling Enterprise 64

DataRobot Blog

SEPTEMBER 1, 2022

For example, a recent IDC study 1 shows that it takes about 290 days on average to deploy a model into production from start to finish. We do not know what the future holds. To prevent delays in productionalizing AI , many organizations invest in MLOps. Your model was accurate yesterday, but what about today?

Metrics 145

Metrics Statistics Modeling Data Science 145

DataRobot Blog

APRIL 29, 2022

With the big data revolution of recent years, predictive models are being rapidly integrated into more and more business processes. This provides a great amount of benefit, but it also exposes institutions to greater risk and consequent exposure to operational losses.

Risk 111

Risk Modeling IT Risk Management 111

Domino Data Lab

JULY 19, 2021

Who takes responsibility for the operationalized models? And how long should the transition between development and deployment last? And how long should the transition between development and deployment last? What does a data scientist do, compared to a data engineer or a DevOps engineer?

Enterprise 85

Enterprise Data Science Testing Modeling 85

IBM Big Data Hub

JULY 6, 2023

What is data science? Areas making up the data science field include mining, statistics, data analytics, data modeling, machine learning modeling and programming. What is machine learning? Machine learning (ML) is a subset of artificial intelligence (AI) that focuses on learning from what the data science comes up with.

Machine Learning 60

Machine Learning Data Science Statistics Deep Learning 60

Domino Data Lab

JULY 28, 2021

If it’s not done successfully, both costs and risk increase. While this has led to exciting discoveries and identified unlimited opportunities, it has also created three significant challenges: complex processes to operationalize models, knowledge silos, and a wild west of tools and infrastructure. Complex Processes.

Data Science 59

Data Science Enterprise Cost-Benefit Data-driven 59

Domino Data Lab

AUGUST 10, 2021

Models are the central output of data science, and they have tremendous power to transform companies, industries, and society. At the center of every machine learning or artificial intelligence application is the ML/AI model that is built with data, algorithms and code. The process of creating models is called modeling.

Modeling 64

Modeling IT Risk Management Deep Learning 64