Organizations faced with a wide range of business challenges are increasingly turning to data-driven approaches to find new and innovative solutions. The emergence of machine learning and AI – and now generative AI – has enabled organizations to leverage their data to form valuable business decisions, create new products, and drive innovation. While data serves as a powerful resource for businesses to leverage, the process of transforming raw data into valuable information and insights is not without its challenges.

Organizations rely on teams of Data Scientists, Data Engineers, and MLOps Engineers to transform data into actionable insights. This is achieved through a process known as the data and AI life cycle, which spans from managing and preparing data, through creating and optimizing AI models, to deployment and monitoring. While the data and AI life cycle has become a crucial component of core business processes, data and AI teams face challenges of time, complexity, and resources at each step. Organizations capable of increasing productivity throughout this life cycle can achieve significant benefits including faster decision-making, lower costs, and greater innovation