Stay informed about AI Governance, Enterprise AI, Responsible AI, and ModelOp Center
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.
Most of this year, enterprises have been reviewing the lessons learned in the past few years from their Enterprise AI initiatives, i.e., what has worked, what hasn’t, and how to move forward to modernize their infrastructures and take full advantage of AI.