Optimization & Deployment
AI Model Optimization and Deployment for Production
Complete workflow for deploying AI models efficiently in production environments
faster after deployment
average deployment time
system uptime
Optimization Frameworks
ONNX
Open standard for AI model exchange between platforms
- Multi-framework support
- ONNX Runtime
- Hardware acceleration
TensorRT
High-performance inference SDK for NVIDIA GPUs
- Layer fusion
- Kernel auto-tuning
- Mixed precision
Intel OpenVINO
Optimization toolkit for Intel hardware
- CPU, GPU, VPU support
- Model optimizer
- Inference engine
TensorFlow Lite
Lightweight solution for mobile and edge devices
- Small model size
- Low power consumption
- Hardware acceleration
Apple CoreML
ML framework for iOS and macOS applications
- Neural Engine support
- On-device processing
- Privacy-focused
ONNX Runtime
High-performance cross-platform inference engine
- Cross-platform
- Auto-optimization
- Multiple execution providers
Deployment Strategies
Deployment Methods
Container Deployment
Use Docker and Kubernetes for scalable management
Cloud Deployment
Leverage cloud services like AWS, Azure, GCP
Edge Deployment
Deploy at network edge for low latency
Hybrid Deployment
Combine on-premise and cloud infrastructure
Essential Tools
Docker & Kubernetes
Container orchestration and auto-scaling
MLflow & Kubeflow
ML lifecycle management and pipeline automation
Prometheus & Grafana
System monitoring and performance tracking
CI/CD Pipelines
Automated testing and deployment pipelines
Ready to Deploy AI Models to Production?
Consult our AI deployment and optimization experts