Managing models in production is challenging. To optimize the value of Artificial Intelligence, AI models must improve efficiency in business applications or support efforts to make better decisions as they run in production. ModelOps is the key capability for scaling and governing enterprise AI initiatives across the organization and ensuring that the maximum value is obtained from such enterprise AI initiatives. This article will talk about the requirements for systems that should be put in place to support this ModelOps capability. We will be drawing examples from real cases that use advanced production enterprise systems to orchestrate and automate the operationalization of models throughout their life cycle for scalable ModelOps.

Managing models in production is challenging. To optimize the value of Artificial Intelligence, AI models must improve efficiency in business applications or support efforts to make better decisions as they run in production. ModelOps is the key capability for scaling and governing enterprise AI initiatives across the organization and ensuring that the maximum value is obtained from such enterprise AI initiatives. This article will talk about the requirements for systems that should be put in place to support this ModelOps capability. We will be drawing examples from real cases that use advanced production enterprise systems to orchestrate and automate the operationalization of models throughout their life cycle for scalable ModelOps.

In recent years, the banking industry has been at the forefront of AI and ML adoption. According to an Economist Intelligence Unit adoption study, 54% of banks and financial institutions with more than 5,000 employees have adopted AI. But AI and ML adoption has not been easy. Difficulty in deployment has been exacerbated by the growing number of new AI platforms, languages, frameworks, and hybrid compute infrastructure. Add to this the fact that models are being developed by staff in multiple business units and AI teams, making it difficult to ensure that the proper risk and regulatory controls and processes are enforced.