This growing popularity compounded with the acceleration of data generation– approximately 120 Zettabytes expected in 2023, a 51% increase over 2 years – highlights the need to process data at greater speeds and volumes, to enable faster decision-making. As this need continues to increase, the most suitable, cost-effective infrastructure is paramount to providing scalable deployment of ML functionality to users. This means customers need to focus on the ease of deployment of ML models, model monitoring, lifecycle management, and model governance. Each of these areas require significant operational investments from an organization to support production-level ML models.