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.

Modern organized enterprises recognize that the adoption of a data-driven strategy is crucial to compete in an increasingly digitalized market. Data and analytics have become a very high priority, rising to the board level, which sees technologies such as Machine Learning and Artificial Intelligence as an opportunity to increase business capabilities, making processes more efficient, and facilitating the spread of new business models. Far and wide, investment in AI and data management are drastically increasing, and new data science projects are underway to build predictive and analytical models for various purposes. However, while companies plan to scale up sophisticated Artificial Intelligence solutions in a reasonable time, the harsh reality is that the adoption of these solutions is often stalled because companies generally focus more on development than on the operationalization of the models. For many non-digital native businesses, the adoption of the data science discipline is often begun with numerous self-contained and fragmented data science teams committed by and large to developing models of Machine Learning and Deep Learning. These small teams of data scientists have sprung up in the varied business units with the aim of building models for different business purposes.

I tend to focus more on technology trends and domains, than specific technologies. Like many of my peers, I'm excited by the opportunity to have our applications run in an environment that is more resilient, has on-demand access to massive amounts of computing power and storage, and of course, the elastic, usage-based model that should drive lower costs. All that said, I need to be pragmatic about our journey, and be sure that we truly realize all these benefits. Yes, "digital transformation" has been overused and diluted by executives and consultants to represent all aspects of digitalization in every facet of a business. But, when you study it, without bias or agendas, an important trend in business modernization is taking shape.

I tend to focus more on technology trends and domains, than specific technologies. Like many of my peers, I'm excited by the opportunity to have our applications run in an environment that is more resilient, has on-demand access to massive amounts of computing power and storage, and of course, the elastic, usage-based model that should drive lower costs. All that said, I need to be pragmatic about our journey, and be sure that we truly realize all these benefits. Yes, "digital transformation" has been overused and diluted by executives and consultants to represent all aspects of digitalization in every facet of a business. But, when you study it, without bias or agendas, an important trend in business modernization is taking shape.