Monitor machine learning models
Artificial intelligence (AI) and machine learning (ML) are now used across industries to address business challenges. For instance, the financial sector may employ AI and ML to address issues with customer segmentation, fraud detection, or loan defaults.
To accomplish this, it's important to keep the following in mind:
Data can reside on-premises (in a customer data center or on AWS cloud).
IBM Cloud Pak for Data can connect with various data sources using a platform connection.
Through the use of data virtualization, many data sources from various locations are connected and combined into a single virtual data view.
The IBM Watson Knowledge Catalog (WKC) ensures that data access and data quality adhere to corporate policies and guidelines.
Curated data can be pushed to Amazon S3 from various data sources using IBM Data Stage or Data Refinery jobs through extract, transform, and load (ETL).
The curated data that’s available in S3 can be used to build and train custom models in Amazon Sage Maker.
IBM Watson Open Scale monitors and evaluates AI model results to make sure they are fair, understandable, and compliant.
To understand organizational data and aid in making wise business decisions, IBM Cognos Analytics integrates with reporting, modeling, and dashboards.