Model Deployment & Serving
Deploy machine learning models securely on-premise, in the cloud, or at the edge. We build containerized serving environments using Docker, Kubernetes, gRPC, and optimized TensorRT to deliver predictions with ultra-low latency.
Docker-packaged deployment ensures identical runs across any environment.
Deploy models as REST/gRPC APIs with optimized batching and rate limiting.
Edge deployment on mobile and IoT devices using ONNX and WebAssembly.
How We Work Step-by-Step
Our systematic approach guarantees modular integration, safety validation, and seamless deployment scaling.
Discovery & Planning
Understanding your business workflow, evaluating model artifacts, and determining baseline latency and throughput targets.
Custom Development
Building scalable AI & SaaS architecture, wrapping models in Docker, optimizing runtime engines (ONNX, TensorRT), and structuring gRPC/REST APIs.
Deployment & Scale
Launching and maintaining the servers, configuring auto-scaling node pools on Kubernetes (AWS/Azure), and applying GitOps continuous deployment.
Monitor & Optimize
Active logging of model input/output distributions, detecting drift, and automating feedback loops for continuous improvement.
API Serving & Edge Pipeline
We leverage cloud-native tools to design isolated microservices. Below is the data-flow topology representing real-time traffic orchestration.
Key Features
- Secure containerized isolation
- Auto-scaling on load spikes
- Full state logging and tracing
Client App
REST / gRPC Request
API Gateway
Rate Limiting & Auth
Triton Server
Dynamic Batching & Inference
Model Registry
S3 Bucket / ONNX / TensorRT
Real-World Deployments
Industry Case Studies & Integration metrics
| Industry | Deployment Type | Infrastructure | Result Impact |
|---|---|---|---|
| E-Commerce | Recommender Systems | AWS ECS + Triton + Redis | <30ms Latency |
| FinTech | Fraud Detection | Kubernetes + gRPC + ONNX | <10ms Inference |
| Healthcare | Mobile-Edge Diagnosis | iOS/Android + CoreML/TFLite | 3x speedup local |