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.
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