Seemingly overnight, machine learning (ML) exited the world of aspirational technology and entered the mainstream. Organizations of every size and across nearly every industry want in on the action—and ML is realistically within reach for all because of the cloud. The cloud brings together data, low-cost storage, security, and ML services along with high-performance CPU- and GPU-based instances for model training and deployment. Now, organizations can store as much data and have as much high-performance compute as they need elastically, so realizing the value of ML can happen much faster. 

However, with the emergence of a wide breadth and depth of cloud infrastructure options and services, it can be difficult to make the right selection for your use case. Many executives are asking, “What factors should I consider when choosing the right cloud compute infrastructure and services for my ML objectives?”