Organizations across various industries are using artificial intelligence (AI) and machine learning (ML) to solve business challenges specific to their industry. For example, in the financial services industry, you can use AI and ML to solve challenges around fraud detection, credit risk prediction, direct marketing, and many others.
Large enterprises sometimes set up a center of excellence (CoE) to tackle the needs of different lines of business (LoBs) with innovative analytics and ML projects.
To generate high-quality and performant ML models at scale, they need to do the following:
Provide an easy way to access relevant data to their analytics and ML CoE
Create accountability on data providers from individual LoBs to share curated data assets that are discoverable, understandable, interoperable, and trustworthy
This can reduce the long cycle time for converting ML use cases from experiment to production and generate business value across the organization.