Deployment Options
Local Deployment
Run models directly on your TopAiPC workstation or server
Pros:
Full control
No latency
Data privacy
Cons:
•Hardware costs
•Maintenance required
Hybrid Deployment
Combine local infrastructure with cloud bursting for peak loads
Pros:
Scalability
Cost optimization
Flexibility
Cons:
•Complexity
•Network dependency
Cluster Deployment
Deploy across multiple systems for distributed workloads
Pros:
High availability
Scalable
Load balancing
Cons:
•Network complexity
•Higher initial cost
Deployment Process
1. Choose Your Stack
Select the right AI framework and tools for your use case (PyTorch, TensorFlow, etc.)
2. Prepare Your Environment
Set up Docker containers, virtual environments, or Kubernetes clusters
3. Configure Models
Download or train your models, optimize for your hardware
4. Deploy & Monitor
Deploy to production and set up monitoring and alerting