Part 7/8:
Implementing Docker allows for heightened control over what the AI model can access while maintaining the ability to use necessary hardware peripherals like GPUs. While this may be overly technical for some users, it brings an additional layer of protection, making it more challenging for external threats to compromise sensitive data.
Conclusion
Running AI models like Deep Seeq R1 locally presents both promising advantages and inherent risks. It advocates data privacy and serves as an alternative to centralized cloud solutions. However, it requires a proactive approach to security, encouraging users to test model behavior and even consider using containerization tools for added safety.