Going Seamless: End-to-End ML Deployment with K8s#

1.1. Why Containerize Your ML Applications#

Deploying an ML model is more than just a matter of saving a trained model to a file; it involves baking in specific libraries, handling hardware dependencies (e.g., GPUs), ensuring consistent inference responses, and guaranteeing scale. Containerization allows you to package your application and its dependencies in a lightweight, portable unit.

Key benefits include:

• Portability across different environments
• Consistent runtime and dependency management
• Easy to replicate environments for debugging and QA
• Streamlined CI/CD workflows

1.2. Containers vs. Virtual Machines#

Traditional virtual machines (VMs) are emulated environments that run entire operating systems. Each VM has its own OS kernel, making them heavier to run. Containers, on the other hand, share the host OS kernel and isolate only the necessary user-space processes. This design makes container footprints much smaller and spinning them up much faster.

1.3. A Glance at Docker#

Docker is the most popular container runtime for building images and running containers. You can define your environment in a Dockerfile—a simple text file that specifies:

1. Base image (e.g., Ubuntu, Python)
2. Required system packages
3. ML libraries (TensorFlow, PyTorch, scikit-learn)
4. Entry point for your inference server

Once built, this image can be pushed to a container registry (like Docker Hub or a private registry) and redeployed consistently anywhere Docker is available.

2.1. Understanding the K8s Architecture#

Kubernetes, commonly referred to as K8s, is an open-source container orchestration platform that manages containerized applications at scale. Key components:

• Master (Control Plane) components: API server, scheduler, and controller manager. They manage cluster-level decisions (e.g., scheduling containers).
• Worker nodes: Run containers. Typically each node has a container runtime, kubelet, and kube-proxy.
• etcd: A distributed key-value store used to persist cluster state.

In an ML deployment context, you might have pipeline steps that each run as separate containers on different nodes. Kubernetes ensures that these containers are scheduled optimally, restarted if they fail, and can communicate with each other securely.

2.2. Pods, Services, and Deployments#

• Pod: The smallest deployable unit in K8s, typically contains one container (though it can have sidecar containers).
• Service: An abstraction that defines a network endpoint for a set of Pods, allowing stable endpoint discovery even when Pods are replaced.
• Deployment: A higher-level abstraction that manages the lifecycle of your Pods, ensuring the desired number of replicas and facilities for rolling updates.

When serving an ML model, you generally wrap it in a Pod that runs your inference code, and then create a Service for external or internal access to that model server. The Deployment object ensures that your model-serving Pods stay healthy.

2.3. Ingress, Networking, and Storage for ML#

• Ingress: Provides a way to externally expose Services. This is especially useful if you want to provide a REST API or a gRPC endpoint for your ML model to external clients.
• Networking: K8s networking can be handled by various plugins that adhere to the Container Network Interface (CNI). For ML serving, you usually just need to ensure your Pod can be reached internally or externally.
• Storage: Persistent Volumes (PV) and Persistent Volume Claims (PVC) allow you to store datasets or pre-trained models. For large-scale ML, you might integrate object storage solutions (e.g., S3, GCS) or network-attached storage.

3.1. Data Processing and Model Training#

Before deployment, your model must be well-defined and stable. Typically, you’ll:

1. Collect and clean data (possibly from multiple sources).
2. Use frameworks like TensorFlow, PyTorch, or scikit-learn to train your model.
3. Validate the performance metrics (e.g., accuracy, F1 score).

You might automate this feature engineering and training process using tools such as Kubeflow Pipelines or Argo Workflows. The final artifact is usually a “frozen” model file (like a .h5 in Keras, a .pt in PyTorch, or a .pkl in scikit-learn).

3.2. Environment Reproducibility#

Nothing is more frustrating than running code that works perfectly on one machine but fails on another. Reproducibility is paramount in ML, where library versions, GPU drivers, and CPU instruction sets matter. You can use:

• Conda environments
• Pip requirements.txt or Poetry
• Docker containers (for ultimate reproducibility)

3.3. Handling GPUs and Specialized Libraries#

ML inference can sometimes leverage GPUs for speed, especially for large neural networks or real-time applications. K8s supports GPU scheduling via node selectors and device plugins. When packaging your model:

• Include the appropriate NVIDIA runtime base image if you plan to use GPU acceleration.
• Make sure your cluster nodes have GPUs installed and configured with correct drivers.
• Adjust your Deployment’s YAML to request GPU resources.

4.1. Dockerfile Anatomy#

Below is a typical Dockerfile for serving a Python-based ML model with Flask. It includes system dependencies, copies the model artifacts, and sets the entry point.

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# Start from a Python base
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FROM python:3.9-slim
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# Install system dependencies
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RUN apt-get update && apt-get install -y --no-install-recommends \
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    gcc \
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    && rm -rf /var/lib/apt/lists/*
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# Create app directory
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WORKDIR /app
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# Copy requirements
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COPY requirements.txt .
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# Install Python dependencies
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RUN pip install --no-cache-dir -r requirements.txt
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# Copy source code
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COPY . .
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# Expose the port
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EXPOSE 5000
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# Set the entry point (start the server)
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CMD ["python", "app.py"]

4.2. Best Practices for ML Containers#

1. Use a minimal base image (e.g., python:3.9-slim) to reduce image size and attack surface.
2. Pin versions of libraries so your container is reproducible.
3. Leverage multi-stage builds for complex dependencies (when applicable).
4. Store large data (e.g., model files) in external storage if possible; keep your image lean.

4.3. Example: Containerizing a Simple Flask-Based Model#

Below is a simple Python script (app.py) that loads a scikit-learn model and exposes a REST endpoint /predict:

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from flask import Flask, request, jsonify
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import joblib
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app = Flask(__name__)
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# Load model
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model = joblib.load('model.pkl')
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@app.route('/predict', methods=['POST'])
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def predict():
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    data = request.get_json()
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    # Example: data = {"features": [5.1, 3.5, 1.4, 0.2]}
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    features = data['features']
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    prediction = model.predict([features])
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    return jsonify({'prediction': prediction.tolist()})
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if __name__ == '__main__':
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    app.run(host='0.0.0.0', port=5000)

• Package this script alongside model.pkl in your Docker image.
• Ensure requirements.txt has flask and scikit-learn.
• Build the image and test locally with Docker.

5.1. Writing Deployment YAML#

Once you have a Docker image in a registry, you can deploy it via a Kubernetes Deployment. Below is an example deployment.yaml:

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apiVersion: apps/v1
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kind: Deployment
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metadata:
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  name: ml-model-deployment
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spec:
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  replicas: 3
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  selector:
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    matchLabels:
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      app: ml-model
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  template:
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    metadata:
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      labels:
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        app: ml-model
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    spec:
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      containers:
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      - name: ml-container
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        image: your-registry/your-ml-image:latest
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        ports:
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        - containerPort: 5000
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        resources:
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          limits:
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            memory: "512Mi"
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            cpu: "500m"
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          requests:
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            memory: "256Mi"
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            cpu: "250m"

5.2. Creating a Service#

A Service will expose your replicated Pods behind a stable DNS name or IP. Below is an example service.yaml:

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apiVersion: v1
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kind: Service
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metadata:
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  name: ml-model-service
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spec:
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  selector:
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    app: ml-model
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  ports:
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    - protocol: TCP
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      port: 80
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      targetPort: 5000
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  type: ClusterIP

• ClusterIP services are accessible from within the cluster.
• NodePort exposes a port on each node in the cluster for external access.
• LoadBalancer is often used on cloud providers to create an external load balancer pointing to your service.

5.3. Versioning and Rolling Updates#

K8s Deployments support rolling updates, allowing you to push a new version of your model with minimal downtime. You might have:

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strategy:
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  type: RollingUpdate
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  rollingUpdate:
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    maxUnavailable: 1
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    maxSurge: 1

This ensures only one Pod is taken down at a time and replaced with the new version, protecting the overall availability of your model-serving service.

6.1. Autoscaling: Horizontal Pod Autoscaler and Vertical Pod Autoscaler#

• Horizontal Pod Autoscaler (HPA): Scales the number of replicas in a Deployment based on CPU/memory usage or even custom metrics. It’s crucial for ML inference, where traffic can be spiky.
• Vertical Pod Autoscaler (VPA): Adjusts the resource requests/limits for existing Pods based on historical usage.

Example HPA configuration for CPU-based scaling:

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apiVersion: autoscaling/v2
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kind: HorizontalPodAutoscaler
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metadata:
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  name: ml-model-hpa
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spec:
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  scaleTargetRef:
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    apiVersion: apps/v1
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    kind: Deployment
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    name: ml-model-deployment
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  minReplicas: 3
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  maxReplicas: 10
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  metrics:
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  - type: Resource
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    resource:
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      name: cpu
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      target:
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        type: Utilization
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        averageUtilization: 70

6.2. Resource Management: CPU, Memory, and Accelerators#

Properly specifying the resource requests/limits helps Kubernetes schedule your Pods. For GPU-based Pods, you’ll need to configure GPU device plugins and request GPU resources, for example:

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resources:
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  limits:
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    nvidia.com/gpu: 1

This ensures your ML container lands on a node that supports GPU workloads.

6.3. Helm for ML Applications#

Helm is a package manager for Kubernetes. It uses “Charts” that bundle K8s YAML definitions, making it easier to parameterize deployments. For complex ML pipelines, you can have a Helm chart that includes:

• A chart for your model server
• Dependencies, such as Redis for caching
• Configuration for an ingress resource

By running helm install my-ml-app ./chart, you can deploy your ML stack in one shot with version control and rollback capabilities.

6.4. Service Mesh and Monitoring#

Tools like Istio, Linkerd, or Consul can provide advanced traffic routing, fault tolerance, and observability. For ML specifically, you might want to:

• Route a small fraction of traffic to a new model version for A/B testing
• Capture request traces to diagnose performance bottlenecks
• Collect metrics on inference latency, memory usage, GPU usage

7.1. Data Ingestion and ETL in K8s#

For a fully automated ML pipeline:

1. Pull data from various sources (databases, object storage).
2. Transform data using Spark, Python scripts, or ETL tools.
3. Store cleaned data in a staging location or a distributed file system.

Kubernetes can orchestrate these steps via CronJobs for scheduled tasks, or through a pipeline tool like Argo Workflows that defines DAGs of containerized tasks.

7.2. Continuous Integration/Continuous Delivery (CI/CD)#

Leverage CI/CD tools (e.g., Jenkins, GitLab CI, GitHub Actions) to automate:

• Building: Trigger Docker image builds whenever code changes.
• Testing: Run unit tests, and performance tests for your ML code.
• Deploying: Roll out updates to your K8s cluster automatically or after manual approval.

7.3. Model Registry and Version Control#

A model registry helps track different versions of your model, storing metadata such as:

• Hyperparameters
• Training data version
• Performance metrics

Tools like MLflow or the Kubeflow Metadata service can integrate with your pipeline for an auditable record of each model version.

7.4. Canary Deployments in Production#

Sometimes you want to release a new version of your model incrementally to a small percentage of users. Canary deployments, facilitated by advanced routing (e.g., via Istio), allow you to compare new model performance on live data before fully switching over.

• Implement traffic-splitting rules (e.g., 10% to the new model).
• Monitor metrics such as response time and accuracy.
• Gradually ramp up traffic if performance is stable, or roll back if issues arise.

8.1. Training a Sample Model#

Suppose you train a simple scikit-learn classification model locally:

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import joblib
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from sklearn.datasets import load_iris
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from sklearn.ensemble import RandomForestClassifier
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iris = load_iris()
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X, y = iris.data, iris.target
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clf = RandomForestClassifier(n_estimators=100, random_state=42)
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clf.fit(X, y)
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joblib.dump(clf, 'model.pkl')

Now you have model.pkl, which you’ll use in your container.

8.2. Building Container Images#

Create a directory with:

• app.py (Flask serving script)
• model.pkl (trained artifact)
• requirements.txt
• Dockerfile

Example requirements.txt:

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Flask==2.0.3
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scikit-learn==1.0.2
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joblib==1.1.0

Next, build your image and push it to a registry:

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docker build -t your-registry/iris-model:1.0 .
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docker push your-registry/iris-model:1.0

Make sure to replace your-registry with your Docker Hub username (or a private registry).

8.3. Writing Docker and Kubernetes Configurations#

We’ve already shown examples of deployment.yaml and service.yaml. After building and pushing your image, update your Deployment file:

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containers:
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- name: iris-model-container
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  image: your-registry/iris-model:1.0
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  ports:
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  - containerPort: 5000

Then, apply to your cluster:

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kubectl apply -f deployment.yaml
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kubectl apply -f service.yaml

8.4. Testing the Inference Service#

If you created a NodePort or LoadBalancer service, you can hit it externally. For example:

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curl -X POST -H "Content-Type: application/json" \
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    -d '{"features": [5.5, 2.3, 4.0, 1.3]}' \
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    http://<node_ip>:<node_port>/predict

Expect to see something like:

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{"prediction": [1]}

9.1. Logging and Monitoring#

• Centralized Logging: Tools like Elasticsearch and Kibana or Splunk can collect logs from your ML containers.
• Metrics and Alerting: Prometheus for scraping metrics and Grafana for dashboards.
• Distributed Tracing: Jaeger or Zipkin help diagnose performance bottlenecks, especially if your pipeline or application is made up of multiple microservices.

9.2. Security and Compliance#

• Role-Based Access Control (RBAC): Limit who can create or delete resources in Kubernetes.
• Network Policies: Control the flow of traffic among Pods.
• Image Scanning: Tools like Clair or Trivy to scan container images for vulnerabilities.
• Secrets Management: Keep credentials, keys, and tokens in Kubernetes Secrets, not in plain config files.

9.3. Disaster Recovery and Backup#

• Immutable Infrastructure: Rebuild nodes from scratch if they fail, rather than patching them in place.
• Backup: Use plugins to back up cluster state (etcd) plus volumes that store crucial data.
• Multi-Cluster Strategy: For mission-critical ML, replicate your cluster in another region or environment for failover.