Projects Tagged model-deployment: Scalable Model Deployment, Inference Pipelines, and MLOps Implementations
Discover projects tagged model-deployment that demonstrate production-ready model deployment best practices, from containerized model serving with Docker and Kubernetes to serverless inference, ONNX runtime optimizations, and GPU-accelerated pipelines. This list surfaces items in the projects nav filtered by the tags pillar to show real-world implementations of model-deployment, detailing CI/CD for ML, model versioning, canary and A/B rollout strategies, monitoring and observability with Prometheus and OpenTelemetry, and cloud-native deployment patterns across AWS, GCP, and Azure. Use the filtering UI to narrow results by framework (TensorFlow, PyTorch, FastAPI), runtime, hardware, or deployment architecture, inspect code links and deployment manifests, compare latency and scalability metrics, and adopt actionable patterns to accelerate production launches—explore, benchmark, and contribute to these reference deployments now.
