The article also explores the pros and cons of deploying AI projects in the cloud versus on-premises. Cloud solutions offer ease of use and access to pre-trained models but can become costly over time. On-premises solutions provide better control over security and latency, making them suitable for regulated industries and latency-sensitive applications. Ultimately, the choice between cloud and on-premises depends on factors such as budget, security needs, and the level of customization required. Pre-validated AI reference architectures can help simplify infrastructure deployment and optimize performance regardless of the deployment environment.
Key takeaways:
- An AI reference architecture is crucial for integrating and optimizing high-performance compute, storage, and network components to maximize GPU power and efficiency.
- Security concerns persist with public cloud deployments, especially for AI data, making on-premises solutions potentially more secure for sensitive information.
- Cloud solutions offer ease of use and lower initial costs but can become expensive over time, while on-premises solutions provide better control and potentially lower long-term costs.
- Businesses can benefit from pre-validated AI reference architectures to simplify infrastructure deployment, optimize performance, and reduce risk.