{"id":69,"date":"2025-06-29T03:13:16","date_gmt":"2025-06-29T03:13:16","guid":{"rendered":"https:\/\/aiopsschool.com\/blog\/?p=69"},"modified":"2025-06-29T03:13:17","modified_gmt":"2025-06-29T03:13:17","slug":"what-is-kfserving","status":"publish","type":"post","link":"https:\/\/aiopsschool.com\/blog\/what-is-kfserving\/","title":{"rendered":"What is KFServing?"},"content":{"rendered":"\n

KFServing<\/strong> is an open-source project designed to simplify and standardize serving machine learning models on Kubernetes<\/strong>. It is now part of KServe<\/a>, which is the next evolution of KFServing.<\/p>\n\n\n\n

\n\n\n\n

What is KFServing (now KServe)?<\/strong><\/h2>\n\n\n\n
    \n
  • KFServing<\/strong> provides a Kubernetes-native way to deploy, serve, and manage machine learning models at scale<\/strong>.<\/li>\n\n\n\n
  • It abstracts the complexities of ML model inference and allows you to deploy models with minimal configuration<\/strong>, using modern cloud-native features like autoscaling, canary rollouts, and multi-framework support.<\/li>\n<\/ul>\n\n\n\n
    \n\n\n\n

    Key Features<\/strong><\/h2>\n\n\n\n
      \n
    1. Multi-Framework Support:<\/strong>\n
        \n
      • Serve models from TensorFlow, PyTorch, scikit-learn, XGBoost, ONNX, HuggingFace Transformers, and more\u2014with a single, unified API<\/strong>.<\/li>\n<\/ul>\n<\/li>\n\n\n\n
      • Kubernetes-Native:<\/strong>\n
          \n
        • Models are deployed as Kubernetes custom resources. KFServing leverages K8s features for scaling, networking, security, and high availability.<\/li>\n<\/ul>\n<\/li>\n\n\n\n
        • Advanced Inference Capabilities:<\/strong>\n
            \n
          • Autoscaling:<\/strong> Scale model servers up or down automatically (even to zero when idle).<\/li>\n\n\n\n
          • Canary Deployments:<\/strong> Safely roll out new model versions with traffic splitting.<\/li>\n\n\n\n
          • GPU\/Accelerator Support:<\/strong> Run inference on GPUs or specialized hardware.<\/li>\n\n\n\n
          • Inferences with Pre\/Post Processing:<\/strong> Support for data transformations before\/after predictions using Python or serverless containers.<\/li>\n<\/ul>\n<\/li>\n\n\n\n
          • Standardized REST\/gRPC APIs:<\/strong>\n
              \n
            • Provides a consistent way for applications to send requests and receive predictions, regardless of the underlying ML framework.<\/li>\n<\/ul>\n<\/li>\n\n\n\n
            • Production-Ready Observability & Logging:<\/strong>\n
                \n
              • Integrates with tools like Prometheus, Grafana, and ELK for monitoring and logging.<\/li>\n<\/ul>\n<\/li>\n\n\n\n
              • Extensibility:<\/strong>\n
                  \n
                • Supports custom inference servers, custom preprocess\/postprocess logic, and advanced ML pipelines.<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n\n\n\n
                  \n\n\n\n

                  How Does KFServing Work?<\/strong><\/h2>\n\n\n\n
                    \n
                  • You define an \u201cInferenceService\u201d YAML<\/strong> (a Kubernetes Custom Resource) describing your model, framework, and storage location.<\/li>\n\n\n\n
                  • KFServing handles everything else: spinning up the container, scaling, networking, versioning, and exposing endpoints.<\/li>\n<\/ul>\n\n\n\n

                    Example: TensorFlow Model InferenceService<\/strong><\/h3>\n\n\n\n
                    apiVersion: \"serving.kubeflow.org\/v1beta1\"\nkind: \"InferenceService\"\nmetadata:\n  name: \"mnist\"\nspec:\n  predictor:\n    tensorflow:\n      storageUri: \"gs:\/\/my-model-bucket\/mnist\/\"\n<\/code><\/pre>\n\n\n\n
                      \n
                    • Deploy with:
                      kubectl apply -f inference_service.yaml<\/code><\/li>\n<\/ul>\n\n\n\n
                      \n\n\n\n
                      Typical Use Cases<\/strong><\/h2>\n\n\n\n
                        \n
                      • Deploying models for online (real-time) inference<\/strong><\/li>\n\n\n\n
                      • A\/B or canary testing of new models<\/strong><\/li>\n\n\n\n
                      • Autoscaling and cost-saving by scaling-to-zero<\/strong><\/li>\n\n\n\n
                      • Managing multiple model frameworks in production<\/strong><\/li>\n\n\n\n
                      • Integrating with MLOps pipelines (Kubeflow Pipelines, Argo, etc.)<\/strong><\/li>\n<\/ul>\n\n\n\n
                        \n\n\n\n
                        KFServing vs Other Serving Tools<\/strong><\/h2>\n\n\n\n
                        Tool<\/th>Strengths<\/th>Limitations<\/th><\/tr><\/thead>
                        KFServing<\/td>Multi-framework, Kubernetes-native, autoscaling, traffic splitting<\/td>Requires Kubernetes, some learning curve<\/td><\/tr>
                        TensorFlow Serving<\/td>Optimized for TensorFlow, standalone<\/td>Single framework<\/td><\/tr>
                        TorchServe<\/td>Optimized for PyTorch<\/td>Single framework<\/td><\/tr>
                        Seldon Core<\/td>Flexible, extensible, multi-framework<\/td>More complex CRDs<\/td><\/tr>
                        BentoML<\/td>Easy model packaging, local\/dev use<\/td>Less cloud-native<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
                        \n\n\n\n
                        Summary<\/strong><\/h2>\n\n\n\n
                        KFServing (KServe)<\/strong> is an advanced, Kubernetes-native way to serve machine learning models in production\u2014with support for autoscaling, version control, traffic management, and multi-framework models\u2014all via easy-to-use Kubernetes resources<\/strong>.<\/p>\n\n\n\n
                        Great for:<\/strong><\/p>\n\n\n\n
                          \n
                        • Teams deploying many models at scale, using Kubernetes<\/li>\n\n\n\n
                        • Anyone wanting to simplify\/standardize production ML inference<\/li>\n<\/ul>\n\n\n\n
                          \n\n\n\n
                          <\/p>\n","protected":false},"excerpt":{"rendered":"
                          KFServing is an open-source project designed to simplify and standardize serving machine learning models on Kubernetes. It is now part […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-69","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/aiopsschool.com\/blog\/wp-json\/wp\/v2\/posts\/69","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/aiopsschool.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/aiopsschool.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/aiopsschool.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/aiopsschool.com\/blog\/wp-json\/wp\/v2\/comments?post=69"}],"version-history":[{"count":1,"href":"https:\/\/aiopsschool.com\/blog\/wp-json\/wp\/v2\/posts\/69\/revisions"}],"predecessor-version":[{"id":70,"href":"https:\/\/aiopsschool.com\/blog\/wp-json\/wp\/v2\/posts\/69\/revisions\/70"}],"wp:attachment":[{"href":"https:\/\/aiopsschool.com\/blog\/wp-json\/wp\/v2\/media?parent=69"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/aiopsschool.com\/blog\/wp-json\/wp\/v2\/categories?post=69"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/aiopsschool.com\/blog\/wp-json\/wp\/v2\/tags?post=69"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}