Accelerate Your Machine Learning Design-to-Production Journey

How Leading Enterprises Operationalize Model Development to Scale AI and Streamline the ML Design-to-Production Lifecycle It’s difficult to talk about the promise of Artificial Intelligence (AI) without talking about one of the greatest challenges to unlocking it: scalability. With the constant acceleration of change in data and analytics, organizations need more from their AI systems than exciting use cases and cutting-edge innovation.

Indeed, “smarter, responsible, scalable AI” was the number one theme in the Gartner Top 10 Data and Analytics Trends for 2021 [1]. Says the same report, “Organizations will begin to require a lot more from AI systems, and they’ll need to figure out how to scale the technologies — something that up to this point has been challenging.” As to why this scalability is so challenging, we have only to look at the size and growth rates of data and data sources, alongside the many different technologies underlying them.