Real-Time ML Inference with Redis Using KubeKanvas

KubeKanvas eliminates undifferentiated heavy lifting in Kubernetes development by visually generating secure, production-grade manifests and Helm charts, reducing time, error rates, and cognitive overload—especially for complex, stateful workloads like real-time ML inference with Redis. This is what we are demonstrating in the example below.

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From Concept to Cluster: Real-Time ML Inference with Redis Using KubeKanvas vs. Manual YAML

In the age of intelligent applications, real-time inference pipelines are increasingly becoming the backbone of modern user experiences—from recommendation engines to fraud detection systems. We set out to build one such cloud-native system: a real-time machine learning inference service backed by a Redis cache, exposing predictions via RESTful APIs and designed to be Kubernetes-native from the ground up. The system needed high availability, secure ingress, controlled network access, and persistent storage for model snapshots.

To evaluate how infrastructure-as-diagram can modernize Kubernetes development, we built the solution in two ways: first, manually authoring every Helm YAML file, and then using KubeKanvas, a visual modeling tool that auto-generates Helm charts and Kubernetes manifests from system architecture diagrams. The contrast between these two approaches tells a compelling story.

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Project Architecture

The system architecture includes a model inference service (Python-based, containerized), a Redis instance as a feature store and cache layer, and a REST API gateway with ingress support. Requests flow from external clients into the inference service via an Ingress controller, which routes traffic through a secure network boundary. Redis requires a StatefulSet with a PersistentVolumeClaim to maintain cache state and preloaded embeddings. Configurations, secrets, and policies ensure the system adheres to enterprise-grade best practices.

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Manual Kubernetes and Helm Development

Designing and implementing the system using Helm required creating a custom chart folder structure with separate templates for each Kubernetes resource. The manual approach included the following resources:

Namespaces were used to logically segment the ML inference stack into its own domain within the cluster, isolating it from other workloads. A namespace manifest (namespace.yaml) was crafted to achieve this separation.

Secrets and ConfigMaps stored sensitive API keys, Redis credentials, and configuration parameters like model paths, cache TTLs, and inference thresholds. These were created using secrets.yaml and configmap.yaml, respectively, and mounted into the pods through volume mounts.

Deployments were used for the stateless inference API, which scaled horizontally and pulled models on startup. A deployment manifest defined the container image, readiness probes, and resource limits. For Redis, a StatefulSet ensured stable network identity and persistent storage using PVCs.

Services exposed both the Redis and inference components to each other and to external traffic via ClusterIP and LoadBalancer types.

Ingress routing was enabled using an Ingress manifest that specified TLS settings, path-based routing rules, and annotations for cert-manager integration.

Network Policies were authored to allow only the inference service to communicate with Redis, and to deny all other inter-pod traffic by default—requiring intricate egress/ingress rule sets and label selectors.

Persistent Volumes and PVCs were provisioned for Redis to retain data across pod restarts. The manifest defined a StorageClass, access mode, and a retention policy.

Here's a sample values.yaml for Redis and the inference service, and a snapshot of Helm template YAMLs:

# values.yaml
redis:
  image: redis:7
  storage:
    size: 2Gi
    className: standard
inference:
  image: ghcr.io/myorg/inference:latest
  replicas: 3
  config:
    modelPath: /models/latest
    cacheTTL: 300
  resources:
    limits:
      cpu: '500m'
      memory: '512Mi'

# templates/statefulset.yaml for Redis
apiVersion: apps/v1
kind: StatefulSet
metadata:
  name: redis
spec:
  serviceName: redis
  replicas: 1
  selector:
    matchLabels:
      app: redis
  template:
    metadata:
      labels:
        app: redis
    spec:
      containers:
        - name: redis
          image: { { .Values.redis.image } }
          volumeMounts:
            - name: redis-data
              mountPath: /data
  volumeClaimTemplates:
    - metadata:
        name: redis-data
      spec:
        accessModes: ['ReadWriteOnce']
        resources:
          requests:
            storage: { { .Values.redis.storage.size } }
        storageClassName: { { .Values.redis.storage.className } }

# templates/ingress.yaml
apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  name: inference-ingress
  annotations:
    cert-manager.io/cluster-issuer: 'letsencrypt-prod'
spec:
  rules:
    - host: inference.myapp.com
      http:
        paths:
          - path: /
            pathType: Prefix
            backend:
              service:
                name: inference-service
                port:
                  number: 80
  tls:
    - hosts:
        - inference.myapp.com
      secretName: inference-tls

Across all the components—namespaces, secrets, configmaps, deployments, statefulsets, services, ingress, network policies, and volumes—the manual Helm authoring consumed approximately 30–35 hours of aggregate effort. This included development, validation, fixing schema issues, debugging policy misconfigurations, and testing in different environments.

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The Burden of Manual YAML

Despite the structured nature of Helm, writing Kubernetes YAMLs manually remains a tedious and error-prone process. It requires deep knowledge of resource schemas, ordering constraints, and best practices that are often missed or inconsistently applied. Particularly with stateful services like Redis and complex security models involving network policies, even minor syntax errors or misreferenced fields can lead to hours of debugging. Furthermore, maintaining this setup across environments or extending it later (e.g., integrating a model retraining job) introduces significant mental overhead. YAML lacks abstraction and readability, and its verbosity makes collaboration hard, version drift common, and automation brittle.

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Visual Modeling with KubeKanvas

KubeKanvas revolutionizes this experience by allowing users to visually construct their cloud-native architecture. In this project, the Redis cache, ML inference pods, ingress gateway, and volume claims were dragged and connected on a canvas. With in-built schema validation and semantic context, KubeKanvas instantly generated Helm charts and Kubernetes manifests, organized within a GitOps-compliant folder structure.

Ingress configuration automatically included TLS certs and routing rules per best practices. Network Policies were inferred from service-to-service connectivity on the canvas, eliminating the need for manual rule crafting. Stateful resources like Redis were scaffolded with PVCs and service bindings pre-wired. Secrets and ConfigMaps were auto-generated and linked to appropriate pods through intuitive dialogs.

From canvas to cluster, the total time to create a production-ready stack was under 2 hours—an order of magnitude faster than the manual approach.

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System Architecture Diagram

The following diagram (compatible with draw.io) visualizes the deployed architecture:

• Ingress → Inference API (Deployment)
• Inference API → Redis (StatefulSet with PVC)
• PersistentVolume bound to Redis
• Network Policies isolating Redis
• ConfigMaps and Secrets injected into services
• Namespace encapsulating all resources

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Conclusion: KubeKanvas and the End of Undifferentiated Heavy Lifting

In a world where time-to-market and infrastructure quality are paramount, KubeKanvas delivers a clear advantage. By eliminating the undifferentiated heavy lifting of YAML authoring, it allows architects and developers to focus on application logic and business value—not plumbing and policy errors. It enforces best practices by design, reduces cognitive overload, and makes Kubernetes development accessible, visual, and collaborative. For teams building modern, cloud-native applications like real-time ML inference pipelines, KubeKanvas is not just a tool—it’s an enabler of velocity, quality, and innovation.