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

AWS Big Data

DECEMBER 4, 2024

Our customers are telling us that they are seeing their analytics and AI workloads increasingly converge around a lot of the same data, and this is changing how they are using analytics tools with their data. Introducing the next generation of SageMaker The rise of generative AI is changing how data and AI teams work together.

Data Analytics 104

Data Analytics Analytics Data Lake Data Quality 104

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

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

JULY 28, 2021

For companies creating models to scale, an enterprise Machine Learning Operation (MLOps) platform not only needs to support enterprise-grade development and production, it needs to follow the same standard process that data scientists use. How Enterprise MLOps Integrates into the DSLC.

Data Science 59

Data Science Enterprise Modeling Experimentation 59