Stay informed about AI Governance, Enterprise AI, Responsible AI, and ModelOp Center
In recent years, the banking industry has been at the forefront of AI and ML adoption. A recent survey by Deloitte Insights shows 70% of all financial services firms use machine learning to manage cash flow, determine credit scores, and protect against cybercrime.
Digital twin technology continues to be adopted by manufacturing industries to support business strategy and gain efficiencies in operations and customer service.
Getting models into production can be difficult, but that isn’t the only challenge you will face with machine learning models throughout their lifetime. Once the model has made it into production, it must be monitored in order to ensure that everything is working properly.
These are the key issues to consider when operationalizing AI at the enterprise level.
Enterprises need AI-ready model governance to drive business value and protect the enterprise against massive regulatory and brand risks.
Model governance means managing the risk of the models running in a healthy state, managing the risk to the business and satisfying any regulatory requirements.
