Machine Learning API Service
Production ML serving stack exposing REST endpoints with model version management, A/B splits, and monitoring hooks. Supports both batch and real-time inference workloads.
FastAPITensorFlowDockerKubernetesPostgreSQLMLflow
Machine Learning API Service
A production-grade serving layer for deploying, versioning, and monitoring machine learning models.
Highlights
- Model registry powered by MLflow with automated promotion gates.
- REST endpoints for both synchronous and batch prediction workflows.
- Traffic splitting rules support A/B testing and gradual rollouts.
- Autoscaling driven by custom metrics and queue depth.
- Drift monitoring hooks emit alerts when performance degrades.
Technical Stack
- FastAPI exposes the API surface with async inference.
- TensorFlow and ONNX runtimes are dynamically loaded and warmed.
- PostgreSQL stores metadata; Redis caches preprocessed features.
- Kubernetes handles orchestration with KEDA for scaling decisions.
Workflow
- Package a model artifact with metadata and log it to MLflow.
- Invoke the deployment CLI to promote a version to staging or production.
- Monitor performance dashboards and compare canary vs. stable models.
Security & Compliance
- Requests are authenticated using API keys and optional JWT claims.
- PII is scrubbed before logging, and audit trails are retained for compliance.
Project Links
Highlights
- Model registry with rollbacks
- Traffic splitting for experimentation
- Autoscaling with KEDA signals
- Metrics and drift monitoring
- Batch + real-time inference
Key Challenge
Drove inference latency under 100ms through async I/O and warmed model caches.