Including ModelOps in your AI strategy - KDnuggets

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.