KFServing is an open-source project designed to simplify and standardize serving machine learning models on Kubernetes. It is now part of KServe, which is the next evolution of KFServing.
What is KFServing (now KServe)?
- KFServing provides a Kubernetes-native way to deploy, serve, and manage machine learning models at scale.
- It abstracts the complexities of ML model inference and allows you to deploy models with minimal configuration, using modern cloud-native features like autoscaling, canary rollouts, and multi-framework support.
Key Features
- Multi-Framework Support:
- Serve models from TensorFlow, PyTorch, scikit-learn, XGBoost, ONNX, HuggingFace Transformers, and more—with a single, unified API.
- Kubernetes-Native:
- Models are deployed as Kubernetes custom resources. KFServing leverages K8s features for scaling, networking, security, and high availability.
- Advanced Inference Capabilities:
- Autoscaling: Scale model servers up or down automatically (even to zero when idle).
- Canary Deployments: Safely roll out new model versions with traffic splitting.
- GPU/Accelerator Support: Run inference on GPUs or specialized hardware.
- Inferences with Pre/Post Processing: Support for data transformations before/after predictions using Python or serverless containers.
- Standardized REST/gRPC APIs:
- Provides a consistent way for applications to send requests and receive predictions, regardless of the underlying ML framework.
- Production-Ready Observability & Logging:
- Integrates with tools like Prometheus, Grafana, and ELK for monitoring and logging.
- Extensibility:
- Supports custom inference servers, custom preprocess/postprocess logic, and advanced ML pipelines.
How Does KFServing Work?
- You define an “InferenceService” YAML (a Kubernetes Custom Resource) describing your model, framework, and storage location.
- KFServing handles everything else: spinning up the container, scaling, networking, versioning, and exposing endpoints.
Example: TensorFlow Model InferenceService
apiVersion: "serving.kubeflow.org/v1beta1"
kind: "InferenceService"
metadata:
name: "mnist"
spec:
predictor:
tensorflow:
storageUri: "gs://my-model-bucket/mnist/"
- Deploy with:
kubectl apply -f inference_service.yaml
Typical Use Cases
- Deploying models for online (real-time) inference
- A/B or canary testing of new models
- Autoscaling and cost-saving by scaling-to-zero
- Managing multiple model frameworks in production
- Integrating with MLOps pipelines (Kubeflow Pipelines, Argo, etc.)
KFServing vs Other Serving Tools
| Tool | Strengths | Limitations |
|---|---|---|
| KFServing | Multi-framework, Kubernetes-native, autoscaling, traffic splitting | Requires Kubernetes, some learning curve |
| TensorFlow Serving | Optimized for TensorFlow, standalone | Single framework |
| TorchServe | Optimized for PyTorch | Single framework |
| Seldon Core | Flexible, extensible, multi-framework | More complex CRDs |
| BentoML | Easy model packaging, local/dev use | Less cloud-native |
Summary
KFServing (KServe) is an advanced, Kubernetes-native way to serve machine learning models in production—with support for autoscaling, version control, traffic management, and multi-framework models—all via easy-to-use Kubernetes resources.
Great for:
- Teams deploying many models at scale, using Kubernetes
- Anyone wanting to simplify/standardize production ML inference