Deep Learning Inference at Scale with AI Readiness

Deep Learning Inference at Scale with AI Readiness

Deep Learning Inference at Scale with AI Readiness

By 2020, deep learning will have reached fundamentally different stage of maturity. Deployment and adoption will no longer be confined to experimentation, becoming a core part of day-to-day business operations across most fields of research and industries.

However, as the AI space is becoming increasingly complex, a one-size-fits-all solution cannot address the unique constraints of each environment across the AI spectrum. In this context, critical hardware considerations include availability, ease of use, and operational expense. What type of infrastructure do you use for your edge devices, workstations or servers today? Do you want to deal with the complexities of multiple architectures?

Exploring these challenges is the subject of this guide.

Sections include:

Determining AI readiness

Developing and deploying data governance and security policies

Infrastructure strategies for the shift to deep learning inference at scale

The magnifying impact of optimized software

Next steps: Breaking barriers between model and reality