Implementation plan
We review your environment, define the architecture, and give you a clear execution path before setup begins.
Secure configuration
Access, firewalls, backups, monitoring, and deployment choices are configured with security and reliability in mind.
Documentation
You receive practical handover notes covering credentials, architecture, operating steps, and next recommendations.
Post-delivery support
After delivery, we stay available for fixes, clarifications, and stabilization during the included support period.
Detailed Description
Jupyter notebooks are for research. Production AI needs proper infrastructure: scalable GPU instances, model versioning, low-latency REST or gRPC APIs, A/B testing, and monitoring for model drift.
We set up end-to-end model serving infrastructure using BentoML, TorchServe, TensorFlow Serving, or Triton Inference Server — deployed on AWS SageMaker, GCP Vertex AI, or custom GPU instances. Your models serve predictions at scale with sub-100ms p99 latency.
What You'll Receive From Us
- Model packaged and versioned (BentoML / TorchServe)
- REST/gRPC API endpoint deployed
- GPU instance configured and benchmarked
- Autoscaling policy configured
- Monitoring (latency, throughput, error rate)
- API documentation (OpenAPI)
- Load test results
What We Need From You
- Trained model file (ONNX, PyTorch, TensorFlow, or Scikit-learn)
- Model input/output schema
- Target latency and throughput requirements
- Cloud provider preference