MLOps & Production Serving
Model serving work across FastAPI services, Dockerized inference, CI/CD, model evaluation, monitoring, and operational reliability.
Built and operated ML inference services with FastAPI, Docker, and Azure DevOps CI/CD pipelines.
Packaged RAG and ML services with health checks, structured errors, monitoring, and demo deployments.
Designed evaluation and model-versioning workflows for comparing model candidates on real data.
Serving Is an Interface Contract
A model service is not only a Python function behind an endpoint. It is a contract around input schema, output schema, latency, errors, model version, logs, and rollback behavior.
The best serving layer makes model behavior inspectable. When an answer changes, it should be possible to identify the model artifact, preprocessing version, feature schema, container image, and deployment environment involved.
FastAPI and Containerized Inference
I use FastAPI when the model needs a clean REST or service boundary. The important details are request validation, startup lifecycle, model loading, health probes, timeout behavior, and explicit error responses.
Docker is useful because it fixes the serving environment. But containerization does not solve model reliability by itself. The container still needs predictable startup, bounded memory use, logging, and dependency discipline.
- Readiness checks that wait for model load
- Structured error handling instead of silent model failures
- Batch and single-request paths where latency and throughput differ
- Clear separation between preprocessing, inference, and postprocessing
CI/CD for ML Services
In ML services, CI/CD should test more than whether the app boots. It should catch schema breaks, regression in sample predictions, dependency issues, and container startup failures.
For risk-sensitive systems, the deployment pipeline should include a small model behavior suite. This does not replace full evaluation, but it catches obvious mistakes before they reach users.
Monitoring and Reliability
I think about monitoring at three levels: service health, model behavior, and data quality. Latency and error rate tell whether the service is alive. Prediction distributions and drift signals tell whether the model is still behaving like the validated version.
Prometheus, Grafana, and CloudWatch-style metrics are useful when the signals are chosen carefully. A dashboard full of generic CPU charts is less useful than a few metrics tied to model behavior and operational failure modes.
- Latency, throughput, error rate, and queue depth
- Input validation failures and malformed payloads
- Prediction distribution changes
- Model version, dataset version, and deployment version traces