Synthetic Data for AI: Definition, Risks, and Strategies

Oct 11, 2022 by David Hendrawirawan in Guest Blogs

ABSTRACT:

Many machine learning projects fail because data scientists don’t have the right data. Techniques such as synthetic data is a novel algorithmic approach to address algorithmic risks.

A Thoughtful Approach to Data Mesh

Oct 11, 2022 by Dave Wells in Data Management

ABSTRACT:

Data Mesh gets a lot of discussion. Some see it as revolutionary—the first new data architecture thinking in years. Others view it as a dangerous backward slide to the chaos of data silos. The reality lies somewhere between—a big shift in architecture thinking with some inherent risk. 

Webinar: Data Access Management: Balancing Data Access and Data Security

Oct 11, 2022 by Jay Piscioneri in Reinventing with Data

ABSTRACT:

Data access management solutions dynamically evaluate every data request against applicable access policies at runtime to determine what data the requester can see.

Declarative Machine Learning and the Future of Data Science

Oct 06, 2022 by Kevin Petrie in Decoding Data Software

ABSTRACT:

Declarative ML has the potential to reduce the time, effort, and skills required to bring ML into production in a wide range of enterprise environments.

Data Orchestration: Simplifying Data Access for Analytics

Oct 05, 2022 by Kevin Petrie in Decoding Data Software

ABSTRACT:

As business demands for analytics rise—along with cloud costs—enterprises need to rationalize how they access and process distributed data.