ML Model Deployment
Your data scientist trained a great model. Now it needs to run in production — containerized, behind an API, with health checks, versioning, and monitoring. We handle that entire gap between 'model.pkl' and a production endpoint your application can call.
Need this done for your project?
We implement, you ship. Async, documented, done in days.
Containerization & API Wrapping
We package your model into a Docker container with a FastAPI or gRPC endpoint. Dependencies get pinned to exact versions. The container includes health checks, readiness probes, and graceful shutdown handling. Model artifacts load from object storage at startup — the container image stays small and the model updates independently from the code.
Infrastructure & Scaling
Deployment targets Kubernetes with resource limits tuned to your model's memory and compute profile. GPU models get scheduled on GPU nodes with proper CUDA runtime. CPU models get horizontal pod autoscaling based on request rate. We configure resource requests to prevent noisy-neighbor issues in shared clusters.
CI/CD for Models
Model updates trigger through your ML pipeline — not manual deploys. A new model version in the registry triggers a deployment pipeline that runs integration tests (does the endpoint return valid predictions?), load tests (does latency hold under expected traffic?), and canary deployment. The whole flow is automated and auditable.
Post-Deployment Monitoring
We wire up prediction logging, input/output distribution tracking, and model performance dashboards. Data drift detection compares incoming feature distributions against training data. Alert rules catch degradation before your users notice. You get full observability into what your model is doing in production.
Why Anubiz Engineering
Ready to get started?
Skip the research. Tell us what you need, and we'll scope it, implement it, and hand it back — fully documented and production-ready.