Resource optimization is critical to AI applications, and the major cloud providers, such as Amazon, Microsoft and Google, give you services to optimize resource usage by dynamically allocating compute power and storage based on workload requirements. For example, autoscaling is integral to resource optimization.

As your developers acclimate to AI development, they can apply their lessons learned to optimize compute power and storage to meet workload requirements for workload efficiency. Your cloud management platform provides the actionable data you need to know about the increased resources your AI applications might consume. Standard practices, such as reducing waste and improving overall performance, are even more critical than deploying traditional applications.

Building the on-premises infrastructure to support AI applications is quite an investment, even if your company has a big budget. Going with AWS, Microsoft Azure and Google Cloud for AI services is economical because the pay-as-you-go pricing model is well suited to the experimentation, learning and impending scalability requirements your organization will have for its AI applications in development.